State-based base calling

ABSTRACT

The technology disclosed relates to state-based base calling. In particular, the technology disclosed relates to incorporating state information about data from previous sequencing cycles into the analysis of data from a current sequencing cycle when generating a base call for the current sequencing cycle. For example, when generating a base call for an Nth sequencing cycle, the technology disclosed can incorporate into the base calling logic state information about data from sequencing cycles 1 to N−1.

PRIORITY APPLICATIONS

This application claims benefit of U.S. Provisional Patent ApplicationNo. 63/247,296, titled “State-Based Base Calling Per-Well State-BasedBase Calling,” filed on Sep. 22, 2021 (Attorney Docket No. ILLM1043-1/IP-2073-PRV); and U.S. Provisional Patent Application No.63/247,301, titled “Compressed State-Based Base Calling SampleSpace-to-Pixel Space State Transformation for Base Calling,” filed onSep. 22, 2021 (Attorney Docket No. ILLM 1043-2/IP-2208-PRV). Thepriority applications are incorporated by reference for all purposes asif fully set forth herein.

FIELD OF THE TECHNOLOGY DISCLOSED

The technology disclosed relates to artificial intelligence typecomputers and digital data processing systems and corresponding dataprocessing methods and products for emulation of intelligence (i.e.,knowledge based systems, reasoning systems, and knowledge acquisitionsystems); and including systems for reasoning with uncertainty (e.g.,fuzzy logic systems), adaptive systems, machine learning systems, andartificial neural networks. In particular, the technology disclosedrelates to using deep neural networks such as deep convolution neuralnetworks for analyzing data.

INCORPORATIONS

The following are incorporated by reference for all purposes as if fullyset forth herein:

U.S. Nonprovisional patent Application No.: ______, titled “COMPRESSEDSTATE-BASED BASE CALLING,” filed contemporaneously (Attorney Docket No.ILLM 1043-4/IP-2208-US);

U.S. Nonprovisional patent application Ser. No. 17/308,035, titled“EQUALIZATION-BASED IMAGE PROCESSING AND SPATIAL CROSSTALK ATTENUATOR,”filed 4 May 2021 (Attorney Docket No. ILLM 1032-2/IP-1991-US);

U.S. Provisional Patent Application No. 63/106,256, titled “SYSTEMS ANDMETHODS FOR PER-CLUSTER INTENSITY CORRECTION AND BASE CALLING,” filed 27Oct. 2020 (Attorney Docket No. ILLM 1034-1/IP-2026-PRV);

U.S. Nonprovisional patent application Ser. No. 15/909,437, titled“OPTICAL DISTORTION CORRECTION FOR IMAGED SAMPLES,” filed on 1 Mar.2018;

U.S. Nonprovisional patent application Ser. No. 16/825,987, titled“TRAINING DATA GENERATION FOR ARTIFICIAL INTELLIGENCE-BASED SEQUENCING,”filed 20 Mar. 2020 (Attorney Docket No. ILLM 1008-16/IP-1693-US);

U.S. Nonprovisional patent application Ser. No. 16/825,991 titled“ARTIFICIAL INTELLIGENCE-BASED GENERATION OF SEQUENCING METADATA,” filed20 Mar. 2020 (Attorney Docket No. ILLM 1008-17/IP-1741-US);

U.S. Nonprovisional patent application Ser. No. 16/826,126, titled“ARTIFICIAL INTELLIGENCE-BASED BASE CALLING,” filed 20 Mar. 2020(Attorney Docket No. ILLM 1008-18/IP-1744-US);

U.S. Nonprovisional patent application Ser. No. 16/826,134, titled“ARTIFICIAL INTELLIGENCE-BASED QUALITY SCORING,” filed 20 Mar. 2020(Attorney Docket No. ILLM 1008-19/IP-1747-US);

U.S. Nonprovisional patent application Ser. No. 16/826,168, titled“ARTIFICIAL INTELLIGENCE-BASED SEQUENCING,” filed 21 Mar. 2020 (AttorneyDocket No. ILLM 1008-20/IP-1752-US);

U.S. Nonprovisional patent application Ser. No. 17/175,546, titled“ARTIFICIAL INTELLIGENCE-BASED BASE CALLING OF INDEX SEQUENCES,” filed12 Feb. 2021 (Attorney Docket No. ILLM 1015-2/IP-1857-US);

U.S. Nonprovisional patent application Ser. No. 17/180,542, titled“ARTIFICIAL INTELLIGENCE-BASED MANY-TO-MANY BASE CALLING,” filed 19 Feb.2021 (Attorney Docket No. ILLM 1016-2/IP-1858-US);

U.S. Nonprovisional patent application Ser. No. 17/176,151, titled“KNOWLEDGE DISTILLATION-BASED COMPRESSION OF ARTIFICIALINTELLIGENCE-BASED BASE CALLER,” filed 15 Feb. 2021 (Attorney Docket No.ILLM 1017-2/IP-1859-US);

U.S. Provisional Patent Application No. 63/072,032, titled “DETECTINGAND FILTERING CLUSTERS BASED ON ARTIFICIAL INTELLIGENCE-PREDICTED BASECALLS,” filed 28 Aug. 2020 (Attorney Docket No. ILLM1018-1/IP-1860-PRV);

U.S. Provisional Patent Application No. 63/161,880, titled “TILELOCATION AND/OR CYCLE BASED WEIGHT SET SELECTION FOR BASE CALLING,”filed 16 Mar. 2021 (Attorney Docket No. ILLM 1019-1/IP-1861-PRV);

U.S. Provisional Patent Application No. 63/161,896, titled “NEURALNETWORK PARAMETER QUANTIZATION FOR BASE CALLING,” filed 16 Mar. 2021(Attorney Docket No. ILLM 1019-2/IP-2049-PRV);

U.S. Nonprovisional patent application Ser. No. 17/176,147, titled“HARDWARE EXECUTION AND ACCELERATION OF ARTIFICIAL INTELLIGENCE-BASEDBASE CALLER,” filed 15 Feb. 2021 (Attorney Docket No. ILLM1020-2/IP-1866-US);

U.S. Provisional Patent Application No. 63/228,954, titled “BASE CALLINGUSING MULTIPLE BASE CALLER MODELS,” filed 3 Aug. 2021 (Attorney DocketNo. ILLM 1021-1/IP-1856-PRV);

U.S. Nonprovisional patent application Ser. No. 17/179,395, titled “DATACOMPRESSION FOR ARTIFICIAL INTELLIGENCE-BASED BASE CALLING,” filed 18Feb. 2021 (Attorney Docket No. ILLM 1029-2/IP-1964-US);

U.S. Nonprovisional patent application Ser. No. 17/180,480, titled“SPLIT ARCHITECTURE FOR ARTIFICIAL INTELLIGENCE-BASED BASE CALLER,”filed 19 Feb. 2021 (Attorney Docket No. ILLM 1030-2/IP-1982-US);

U.S. Nonprovisional patent application Ser. No. 17/180,513, titled “BUSNETWORK FOR ARTIFICIAL INTELLIGENCE-BASED BASE CALLER,” filed 19 Feb.2021 (Attorney Docket No. ILLM 1031-2/IP-1965-US);

U.S. Provisional Patent Application No. 63/169,163, titled “ARTIFICIALINTELLIGENCE-BASED BASE CALLER WITH CONTEXTUAL AWARENESS,” filed 31 Mar.2021 (Attorney Docket No. ILLM 1033-1/IP-2007-PRV);

U.S. Provisional Patent Application No. 63/216,419, titled “SELF-LEARNEDBASE CALLER, TRAINED USING OLIGO SEQUENCES,” filed 29 Jun. 2021(Attorney Docket No. ILLM 1038-1/IP-2050-PRV);

U.S. Provisional Patent Application No. 63/216,404, titled “SELF-LEARNEDBASE CALLER, TRAINED USING ORGANISM SEQUENCES,” filed 29 Jun. 2021(Attorney Docket No. ILLM 1038-2/IP-2094-PRV);

U.S. Provisional Patent Application No. 63/223,408, titled “SPECIALISTSIGNAL PROFILERS FOR BASE CALLING,” filed 19 Jul. 2021 (Attorney DocketNo. ILLM 1041-1/IP-2063-PRV);

U.S. Provisional Patent Application No. 63/226,707, titled “QUALITYSCORE CALIBRATION OF BASECALLING SYSTEMS,” filed 28 Jul. 2021 (AttorneyDocket No. ILLM 1045-1/IP-2093-PRV);

U.S. Provisional Patent Application No. 63/217,644, titled “EFFICIENTARTIFICIAL INTELLIGENCE-BASED BASE CALLING OF INDEX SEQUENCES,” filed 1Jul. 2021 (Attorney Docket No. ILLM 1046-1/IP-2135-PRV);

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U.S. Nonprovisional patent application Ser. No. 13/624,200, titled“METHODS AND COMPOSITIONS FOR NUCLEIC ACID SEQUENCING,” filed on 21 Sep.2012, (Attorney Docket No. IP-0538-US);

U.S. Nonprovisional patent application Ser. No. 13/006,206, titled “DATAPROCESSING SYSTEM AND METHODS,” filed on 13 Jan. 2011;

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BACKGROUND

The subject matter discussed in this section should not be assumed to beprior art merely as a result of its mention in this section. Similarly,a problem mentioned in this section or associated with the subjectmatter provided as background should not be assumed to have beenpreviously recognized in the prior art. The subject matter in thissection merely represents different approaches, which in and ofthemselves can also correspond to implementations of the claimedtechnology.

The rapid improvement in computation capability has made deepconvolution neural networks (CNNs) a great success in recent years onmany computer vision tasks with significantly improved accuracy. Duringthe inference phase, many applications demand low latency processing ofone image with strict power consumption requirement, which reduces theefficiency of graphics processing unit (GPU) and other general-purposeplatform, bringing opportunities for specific acceleration hardware,e.g., field programmable gate array (FPGA), by customizing the digitalcircuit specific for the deep learning algorithm inference. However,deploying CNNs on portable and embedded systems is still challenging dueto large data volume, intensive computation, varying algorithmstructures, and frequent memory accesses.

As convolution contributes most operations in CNNs, the convolutionacceleration scheme significantly affects the efficiency and performanceof a hardware CNN accelerator. Convolution involves multiply andaccumulate (MAC) operations with four levels of loops that slide alongkernel and feature maps. The first loop level computes the MAC of pixelswithin a kernel window. The second loop level accumulates the sum ofproducts of the MAC across different input feature maps. After finishingthe first and second loop levels, a final output pixel is obtained byadding the bias. The third loop level slides the kernel window within aninput feature map. The fourth loop level generates different outputfeature maps.

FPGAs have gained increasing interests and popularity in particular toaccelerate the inference tasks, due to their (1) high degree ofreconfigurability, (2) faster development time compared to applicationspecific integrated circuits (ASICs) to catch up with the rapid evolvingof CNNs, (3) good performance, and (4) superior energy efficiencycompared to GPUs. The high performance and efficiency of an FPGA can berealized by synthesizing a circuit that is customized for a specificcomputation to directly process billions of operations with thecustomized memory systems. For instance, hundreds to thousands ofdigital signal processing (DSP) blocks on modern FPGAs support the coreconvolution operation, e.g., multiplication and addition, with highparallelism. Dedicated data buffers between external on-chip memory andon-chip processing engines (PEs) can be designed to realize thepreferred dataflow by configuring tens of Mbyte on-chip block randomaccess memories (BRAM) on the FPGA chip.

Efficient dataflow and hardware architecture of CNN acceleration aredesired to minimize data communication while maximizing resourceutilization to achieve high performance. An opportunity arises to designmethodology and framework to accelerate the inference process of variousCNN algorithms on acceleration hardware with high performance,efficiency, and flexibility.

The key feature of next generation sequencing (NGS) technologies isparallelization and the main mechanism underlying several sequencingplatforms is sequencing-by-synthesis (SBS). Briefly, tens to hundreds ofmillions of random DNA fragments get sequenced simultaneously bysequentially building up complementary bases of single-stranded DNAtemplates and by capturing the synthesis information in a series of rawimages of fluorescence.

Extracting the actual sequence information (i.e., strings in {A, C, G,T}) from image data involves two computational tasks, namely imageanalysis and base calling. The primary function of image analysis is totranslate image data into fluorescence intensity data for each DNAfragment, while the goal of base calling is to infer sequenceinformation from the obtained intensity data.

There are a number of stochastic and contextual sources of variationthat can reduce base calling accuracy. For example, k-mer biases in basecalling are affected by GC content of the sequenced genome. Base callerscan exhibit bias when applied to GC-rich regions of DNA, primarily dueto reduced sequence complexity but also as a result of polymerase chainreaction (PCR) bias during amplification steps.

The accuracy of base calling is of essential importance for variousdownstream applications including sequence assembly, SNP calling, andgenotype calling. Improving base calling accuracy can enable achievingdesired performance of downstream applications with smaller sequencingcoverage, which translates to a reduction in the sequencing cost.

Training neural networks for base calling requires large amounts ofcomputer memory, which increases exponentially with increasing imagesize and numerosity. Computer memory becomes a limiting factor becausethe backpropagation algorithm for optimizing deep neural networksrequires the storage of intermediate activations. Since the size andnumerosity of these intermediate activations increases proportionate tothe input size and numerosity, memory quickly fills up with larger andmore images.

Base callers that use neural networks, for example, the ones disclosedin commonly owned patent application Ser. Nos. 16/826,126; 16/826,134;16/826,168; 17/175,546; 17/180,542; 17/176,151; 63/072,032; 63/161,880;63/161,896; 17/176,147; 63/228,954; 17/179,395; 17/180,480; 17/180,513;63/169,163; and 63/217,644, make a base call prediction using image datafor a sliding window of sequencing cycles, according to oneimplementation. Increasing the size of the sliding window to includeimage data from more sequencing cycles would increase complexity of theneural networks and also add additional burden on available compute andmemory.

An opportunity arises to configure base calling operations toincorporate contextual information from a multitude of past sequencingcycles. More accurate base calling with reduced error rates,particularly towards attenuating k-mer bias, may result.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

The color drawings also may be available in PAIR via the SupplementalContent tab. In the drawings, like reference characters generally referto like parts throughout the different views. Also, the drawings are notnecessarily to scale, with an emphasis instead generally being placedupon illustrating the principles of the technology disclosed. In thefollowing description, various implementations of the technologydisclosed are described with reference to the following drawings, inwhich.

FIG. 1 is a high-level diagram of the disclosed state-based basecalling.

FIG. 2 illustrates one implementation of executing a base callingoperation using state information generated based on historic intensityvalues of pixels in the sequencing images.

FIG. 3 illustrates one implementation of generating per-pixel states forpixels in sequencing images processed as input for base calling.

FIG. 4 illustrates one implementation of generating base calls based onthe per-pixel states.

FIG. 5 illustrates one implementation of generating per-channel statesfor pixels in sequencing images processed as input for base calling.

FIG. 6 illustrates one implementation of generating per-channel MIN(minimum) states for pixels in sequencing images processed as input forbase calling.

FIG. 7 illustrates one implementation of generating pan-channel MIN(minimum) states for pixels in sequencing images processed as input forbase calling.

FIG. 8 illustrates one implementation of generating per-channel MAX(maximum) states for pixels in sequencing images processed as input forbase calling.

FIG. 9 illustrates one implementation of generating pan-channel MAX(maximum) states for pixels in sequencing images processed as input forbase calling.

FIG. 10 illustrates one implementation of generating per-channel AVG(average) states for pixels in sequencing images processed as input forbase calling.

FIG. 11 illustrates one implementation of generating pan-channel AVG(average) states for pixels in sequencing images processed as input forbase calling.

FIG. 12 illustrates one implementation of generating per-channel MIN MAX(minimum and maximum) states 1200 for pixels in sequencing imagesprocessed as input for base calling.

FIG. 13 illustrates one implementation of generating per-channel MIN AVG(minimum and average) states 1300 for pixels in sequencing imagesprocessed as input for base calling.

FIG. 14 illustrates one implementation of generating per-channel MAX AVG(maximum and average) states 1400 for pixels in sequencing imagesprocessed as input for base calling.

FIG. 15 illustrates one implementation of generating per-channel MIN MAXAVG (minimum, maximum, and average) states for pixels in sequencingimages processed as input for base calling.

FIG. 16 illustrates one implementation of generating state informationin dependence upon previously called bases for use in future basecalling.

FIGS. 17A, 17B, and 17C illustrate base call-directed ON and OFF stategeneration for a current sequencing cycle eleven (Cycle 11).

FIGS. 18A, 18B, and 18C expand on FIGS. 17A, 17B, and 17C and illustratehow the OFF state is updated at a next sequencing cycle twelve (Cycle12).

FIGS. 19A, 19B, and 19C further expand on FIGS. 18A, 18B, and 18C andillustrate how the ON state is updated at a yet next sequencing cyclethirteen (Cycle 13).

FIGS. 20A, 20B, and 20C further expand on FIGS. 19A, 19B, and 19C andillustrate how both the ON and OFF states are updated at a yet nextsequencing cycle fourteen (Cycle 14).

FIG. 21 illustrates one implementation of generating state informationfor base calling using exponential weighted averaging.

FIG. 22 illustrates one implementation of generating per-channel stateinformation for base calling using exponential weighted averaging.

FIG. 23 depicts an example of generating per-channel state informationfor base calling using exponential weighted averaging.

FIG. 24 illustrates one implementation of generating per-channel stateinformation for base calling using exponential weighted averaging thatis directed by previously called bases.

FIG. 25 depicts an example of generating per-channel state informationfor base calling using exponential weighted averaging that is directedby previously called bases.

FIG. 26 illustrates one implementation of generating state informationfor non-cluster pixels using previously called bases.

FIG. 27 illustrates different implementations of generating state datafor base calling.

FIG. 28 illustrates different implementations of feeding state data atvarious aspects of processing of a base calling operation.

FIG. 29 illustrates one implementation of state-based and neuralnetwork-based base calling.

FIG. 30 illustrates one implementation of using per-cluster state datafor base calling clusters.

FIG. 31 illustrates one implementation of generating per-cluster statesusing historic intensity values of corresponding cluster pixels.

FIG. 32 illustrates one implementation of generating per-cluster statesusing historic feature values of spatially convolved featurescorresponding to cluster pixels.

FIG. 33 illustrates one implementation of generating cluster intensitiesby interpolating pixel intensities, and using the interpolated clusterintensities to generate per-cluster states for base calling.

FIG. 34 illustrates one implementation of generating per-cluster statesusing a Real Time Analysis (RTA) base caller separate from the neuralnetwork-based base caller.

FIG. 35 illustrates one implementation of a compression logic thatgenerates compressed features.

FIG. 36 illustrates one implementation of generating per-cluster statesusing historic feature values of compressed spatially convolved featurescorresponding to cluster pixels.

FIG. 37 illustrates one implementation of generating per-cluster statesusing the RTA base caller separate from the neural network-based basecaller for incorporation of the per-cluster states with compressedfeature.

FIG. 38 illustrates one implementation of generating per-cluster statesusing historic intensity values of corresponding cluster pixels forincorporation of the per-cluster states with compressed features.

FIG. 39 illustrates one implementation of generating dense, per-pixelstates from sparse, per-well states.

FIG. 40 illustrates one implementation of providing the sparse, per-wellstates, the dense, per-pixel states, and per-pixel intensity values asinput to the base caller for executing base calling operations.

FIG. 41 illustrates one implementation of using the sparse, per-wellstates, the dense, per-pixel states, and per-pixel intensity values asinput to the neural network-based base caller for executing base callingoperations.

FIGS. 42A and 42B depict one implementation of a sequencing system thatcomprises a configurable processor.

FIG. 42C is a simplified block diagram of a system for analysis ofsensor data from the sequencing system, such as base call sensoroutputs.

FIG. 43A is a simplified diagram showing aspects of the base callingoperation, including functions of a runtime program executed by a hostprocessor.

FIG. 43B is a simplified diagram of a configuration of a configurableprocessor.

FIG. 44 illustrates one implementation of determining state data on aCPU, and loading the state data from the CPU to an FPGA for basecalling.

FIG. 45 compares base calling performance of a non-neural network-basedbase caller RTA, a neural network-based base caller DeepRTA (5ci_k14)without state information, and the disclosed state-based and neuralnetwork-based base caller DeepRTA with State (5ci_k14_DC) that usesper-pixel MIN state information as additional/supplemental input.

FIG. 46 compares base calling performance of the disclosed state-basedand neural network-based base caller DeepRTA with State (5ci_k14_DC)across different state channels.

FIG. 47 illustrates base calling performance of the disclosedstate-based and neural network-based base caller DeepRTA with State (DC)on k-mers, specifically 5mers. 5mers refers to repetitive base patternsfor five base positions (e.g., ACGCG, GGGGG, TCGCG).

FIG. 48 compares base calling performance of a non-neural network-basedbase caller RTA, a neural network-based base caller DeepRTA withoutstate information (5ci_k14), the disclosed state-based and neuralnetwork-based base caller DeepRTA with State (5ci_k14_DC_MIN) that usesper-pixel MIN state information as additional/supplemental input, andthe disclosed state-based and neural network-based base caller DeepRTAwith State (5ci_k14_DC_MIN_MAX) that uses per-pixel MIN and MAX stateinformation as additional/supplemental input.

FIG. 49 compares base calling performance of a non-neural network-basedbase caller RTA, the neural network-based base caller DeepRTA withoutstate information and 5-cycle flanking input window (5ci_k14), thedisclosed state-based and neural network-based base caller DeepRTA withState (5ci_k14_DC_MIN) that uses per-pixel MIN state information asadditional/supplemental input along with 5-cycle flanking input window,the disclosed state-based and neural network-based base caller DeepRTAwith State (3ci_k14_DC_MIN_MAX) that uses per-pixel MIN and MAX stateinformation as additional/supplemental input along with 3-cycle flankinginput window, and the disclosed state-based and neural network-basedbase caller DeepRTA with State (5ci_k14_DC_MIN_MAX) that uses per-pixelMIN and MAX state information as additional/supplemental input alongwith 5-cycle flanking input window.

FIG. 50 compares base calling performance of the disclosed state-basedand neural network-based base caller DeepRTA with State(5ci_k14_DC_MIN_MAX) that uses per-pixel MIN and MAX state informationas additional/supplemental input along with 5-cycle flanking inputwindow on k-mers (specifically 5mers) against that of the neuralnetwork-based base caller DeepRTA without state information and 5-cycleflanking input window (5ci_k14). 5mers refers to repetitive basepatterns for five base positions (e.g., ACGCG, GGGGG, TCGCG).

FIG. 51 compares base calling performance of a non-neural network-basedbase caller RTA with an Equalizer implementation, the disclosedstate-based and neural network-based base caller DeepRTA with State(5ci_k14_DC_MIN_MAX) that uses per-pixel MIN and MAX state informationas additional/supplemental input along with 5-cycle flanking inputwindow, and the disclosed state-based and neural network-based basecaller DeepRTA with State (5ci_k14_DC_AVG) that uses per-pixel AVG stateinformation as additional/supplemental input along with 5-cycle flankinginput window.

FIG. 52 compares base calling performance of the non-neuralnetwork-based base caller RTA with the Equalizer implementation and thedisclosed state-based and neural network-based base caller DeepRTA withState (5ci_k14_DC_MIN_MAX_AVG) that uses per-pixel MIN, MAX, and AVGstate information as additional/supplemental input along with 5-cycleflanking input window.

FIG. 53 compares base calling performance of the non-neuralnetwork-based base caller RTA with the Equalizer implementation, thedisclosed state-based and neural network-based base caller DeepRTA withState (5ci_k14_DC_MIN_MAX) that uses per-pixel MIN and MAX stateinformation as additional/supplemental input along with 5-cycle flankinginput window and filter banks of size 14 (K=14), and the disclosedstate-based and neural network-based base caller DeepRTA with State(5ci_k14_DC_MIN_MAX) that uses per-pixel MIN and MAX state informationas additional/supplemental input along with 5-cycle flanking inputwindow and filter banks of size 32 (K=32).

FIG. 54 compares base calling performance of the disclosed state-basedand neural network-based base caller DeepRTA with State for differentalpha parameter configurations (e.g., 0.05, 0.07, 0.10, 0.12) ofexponential weighted averaging implementations, as discussed above.

FIGS. 55 and 56 are graphs that track the state values determined byexponential weighted averages according to one implementation of thetechnology disclosed.

FIG. 57 compares base calling performance of RTA with the Equalizerimplementation (RTA+Eq), the DeepRTA without state information (DeepRTA(k14_1m)), and the disclosed DeepRTA with State (DeepRTA_extrachannels(k14_1,_onoff_expavg)) from the perspective of secondary analysis tasksand metrics like number of called reads, read mismatch error rate forRead 1 and Read 1 and their average, single nucleotide polymorphism(SNP) recall, SNO precision, SNP calling accuracy (F1 score),insertion/deletion (Indel) recall, Indel precision, and Indel callingaccuracy (F1 score).

DETAILED DESCRIPTION

The following discussion is presented to enable any person skilled inthe art to make and use the technology disclosed and is provided in thecontext of a particular application and its requirements. Variousmodifications to the disclosed implementations will be readily apparentto those skilled in the art, and the general principles defined hereincan be applied to other implementations and applications withoutdeparting from the spirit and scope of the technology disclosed. Thus,the technology disclosed is not intended to be limited to theimplementations shown but is to be accorded the widest scope consistentwith the principles and features disclosed herein.

The detailed description of various implementations will be betterunderstood when read in conjunction with the appended drawings. To theextent that the figures illustrate diagrams of the functional blocks ofthe various implementations, the functional blocks are not necessarilyindicative of the division between hardware circuitry. Thus, forexample, one or more of the functional blocks (e.g., modules,processors, or memories) may be implemented in a single piece ofhardware (e.g., a general purpose signal processor or a block of randomaccess memory, hard disk, or the like) or multiple pieces of hardware.Similarly, the programs may be stand-alone programs, may be incorporatedas subroutines in an operating system, may be functions in an installedsoftware package, and the like. It should be understood that the variousimplementations are not limited to the arrangements and instrumentalityshown in the drawings.

The processing engines and databases of the figures, designated asmodules, can be implemented in hardware or software, and need not bedivided up in precisely the same blocks as shown in the figures. Some ofthe modules can also be implemented on different processors, computers,or servers, or spread among a number of different processors, computers,or servers. In addition, it will be appreciated that some of the modulescan be combined, operated in parallel or in a different sequence thanthat shown in the figures without affecting the functions achieved. Themodules in the figures can also be thought of as flowchart steps in amethod. A module also need not necessarily have all its code disposedcontiguously in memory; some parts of the code can be separated fromother parts of the code with code from other modules or other functionsdisposed in between.

INTRODUCTION

The technology disclosed relates to state-based base calling. Inparticular, the technology disclosed relates to incorporating stateinformation about data from previous sequencing cycles into the analysisof data from a current sequencing cycle when generating a base call forthe current sequencing cycle. For example, when generating a base callfor an Nth sequencing cycle, the technology disclosed can incorporateinto the base calling logic state information about data from sequencingcycles 1 to N−1.

The following discussion describes various implementations of thedisclosed state-based base calling. The implementations vary by virtueof different data and processing aspects. For example, different“methods” or “logics” of generating state information yield differenttypes of states. Also, “the thing” whose historic values are tracked forstate generation can vary from implementation to implementation.Furthermore, once generated, “when,” “where,” and “how” the stateinformation is processed for base calling give rise to variedimplementations of the technology disclosed.

State-Based Base Calling

FIG. 1 is a high-level diagram of the disclosed state-based base calling100. Base calling is the process of determining the nucleotidecomposition of a sequence. In one implementation, base calling involvesanalyzing image data, i.e., sequencing images, produced during asequencing run (or sequencing reaction) carried out by a sequencingsystem such as Illumina's iSeq, HiSeqX, HiSeq 3000, HiSeq 4000, HiSeq2500, NovaSeq 6000, NextSeq 550, NextSeq 1000, NextSeq 2000, NextSeqDx,MiSeq, and MiSeqDx. In other implementations, base calling can involveinferring sequence reads from non-image sequencing data.

Sequencing system 104 can be used for the sequencing of nucleic acids.Applicable techniques include those where nucleic acids are attached atfixed locations in an array (e.g., the wells of a flow cell) and thearray is imaged repeatedly. In such implementations, the sequencingsystem 104 can obtain images in two different color channels, which canbe used to distinguish a particular nucleotide base type from another.More particularly, the sequencing system 104 can implement a processreferred to as “base calling,” which generally refers to a process of adetermining a base call (e.g., adenine (A), cytosine (C), guanine (G),or thymine (T)) for a given spot location of an image at an imagingcycle. During two-channel base calling, for example, image dataextracted from two images can be used to determine the presence of oneof four base types by encoding base identity as a combination of theintensities of the two images. For a given spot or location in each ofthe two images, base identity can be determined based on whether thecombination of signal identities is [on, on], [on, off], [off, on], or[off, off].

Output data from the sequencing system 104 can be communicated to a realtime analysis module (not shown). Real time analysis module, in variousimplementations, executes computer readable instructions for analyzingthe image data (e.g., image quality scoring, base calling, etc.),reporting or displaying the characteristics of the beam (e.g., focus,shape, intensity, power, brightness, position) to a graphical userinterface (GUI), etc. These operations can be performed in real-timeduring imaging cycles to minimize downstream analysis time and providereal time feedback and troubleshooting during an imaging run. Inimplementations, real time analysis module can be a computing devicethat is communicatively coupled to and controls an imaging sub-system ofthe sequencing system 104.

The following discussion outlines how the sequencing images aregenerated and what they depict, in accordance with one implementation.

In some implementations, base calling decodes the intensity data encodedin the sequencing images into nucleotide sequences. In oneimplementation, the Illumina sequencing platforms employ cyclicreversible termination (CRT) chemistry for base calling. The processrelies on growing nascent strands complementary to template strands withfluorescently-labeled nucleotides, while tracking the emitted signal ofeach newly added nucleotide. The fluorescently-labeled nucleotides havea 3′ removable block that anchors a fluorophore signal of the nucleotidetype.

Sequencing occurs in repetitive cycles, each comprising three steps: (a)extension of a nascent strand by adding the fluorescently-labelednucleotide; (b) excitation of the fluorophore using one or more lasersof an optical sub-system of the sequencing system 104 and imagingthrough different filters of the optical sub-system, yielding thesequencing images; and (c) cleavage of the fluorophore and removal ofthe 3′ block in preparation for the next sequencing cycle. Incorporationand imaging cycles are repeated up to a designated number of sequencingcycles, defining the read length. Using this approach, each cycleinterrogates a new position along the template strands.

The tremendous power of the Illumina sequencers stems from their abilityto simultaneously execute and sense millions or even billions ofclusters (also called “clusters”) undergoing CRT reactions. A clustercomprises approximately one thousand identical copies of a templatestrand, though clusters vary in size and shape. The clusters are grownfrom the template strand, prior to the sequencing run, by bridgeamplification or exclusion amplification of the input library. Thepurpose of the amplification and cluster growth is to increase theintensity of the emitted signal since the imaging device cannot reliablysense fluorophore signal of a single strand. However, the physicaldistance of the strands within a cluster is small, so the imaging deviceperceives the cluster of strands as a single spot.

Sequencing occurs in a flow cell (or biosensor)—a small glass slide thatholds the input strands. The flow cell is connected to the opticalsystem, which comprises microscopic imaging, excitation lasers, andfluorescence filters. The flow cell comprises multiple chambers calledlanes. The lanes are physically separated from each other and maycontain different tagged sequencing libraries, distinguishable withoutsample cross contamination. In some implementations, the flow cellcomprises a patterned surface. A “patterned surface” refers to anarrangement of different regions in or on an exposed layer of a solidsupport.

The imaging device of the sequencing system 104 (e.g., a solid-stateimager such as a charge-coupled device (CCD) or a complementarymetal-oxide-semiconductor (CMOS) sensor) takes snapshots at multiplelocations along the lanes in a series of non-overlapping regions calledtiles. For example, there can be sixty four or ninety six tiles perlane. A tile holds hundreds of thousands to millions of clusters.

The output of the sequencing run is the sequencing images. Sequencingimages depict intensity emissions of the clusters and their surroundingbackground using a grid (or array) of pixelated units (e.g., pixels,superpixels, subpixels). The intensity emissions are stored as intensityvalues of the pixelated units. The sequencing images have dimensions w xh of the grid of pixelated units, where w (width) and h (height) are anynumbers ranging from 1 and 100,000 (e.g., 115×115, 200×200, 1800×2000,2200×25000, 2800×3600, 4000×400). In some implementations, w and h arethe same. In other implementations, w and h are different. Thesequencing images depict intensity emissions generated as a result ofnucleotide incorporation in the nucleotide sequences during thesequencing run. The intensity emissions are from associated clusters andtheir surrounding background.

A data flow logic (not shown) provides the sequencing images to a basecaller 144 for base calling, in accordance with one implementation. Thebase caller 144 accesses the sequencing images on a patch-by-patch basis(or a tile-by-tile basis), in accordance with one implementation. Eachof the patches is a sub-grid (or sub-array) of pixelated units in thegrid of pixelated units that forms the sequencing images. The patcheshave dimensions q×r of the sub-grid of pixelated units, where q (width)and r (height) are any numbers ranging from 1 and 10000 (e.g., 3×3, 5×5,7×7, 10×10, 15×15, 25×25, 64×64, 78×78, 115 x 115). In someimplementations, q and r are the same. In other implementations, q and rare different. In some implementations, the patches extracted from asequencing image are of the same size. In other implementations, thepatches are of different sizes. In some implementations, the patches canhave overlapping pixelated units (e.g., on the edges).

Sequencing produces m sequencing images per sequencing cycle forcorresponding m image channels, in accordance with some implementations.That is, each of the sequencing images has one or more image (orintensity) channels (analogous to the red, green, blue (RGB) channels ofa color image). In one implementation, each image channel corresponds toone of a plurality of filter wavelength bands. In anotherimplementation, each image channel corresponds to one of a plurality ofimaging events at a sequencing cycle. In yet another implementation,each image channel corresponds to a combination of illumination with aspecific laser and imaging through a specific optical filter. The imagepatches are tiled (or accessed) from each of the m image channels for aparticular sequencing cycle. In different implementations such as 4-,2-, and 1-channel chemistries, m is 4 or 2. In other implementations, mis 1, 3, or greater than 4. In other implementations, the images can bein blue and violet color channels instead of or in addition to the redand green color channels.

Consider, for example, that a sequencing run is implemented using twodifferent image channels: a blue channel and a green channel. Then, ateach sequencing cycle, the sequencing run produces a blue image and agreen image. This way, for a series of k sequencing cycles of thesequencing run, a sequence of k pairs of blue and green images isproduced as output and stored as the sequencing images. Accordingly, asequence of k pairs of blue and green image patches is generated for thepatch-level processing by the base caller 144.

The sequencing system 104 can also generate non-image sequencing data,in accordance with other implementations. In one implementation, thesequencing data can be based on pH changes induced by the release ofhydrogen ions during molecule extension. The pH changes can be detectedand converted to a voltage change that is proportional to the number ofbases incorporated. In yet another implementation, the sequencing datacan be constructed, for example, from nanopore sensing that usesbiosensors to measure the disruption in current as a cluster passesthrough a nanopore or near its aperture while determining the identityof the base. In one implementation, the nanopore-based sequencing can bebased on the following concept: pass a single strand of DNA (or RNA)through a membrane via a nanopore and apply a voltage difference acrossthe membrane. The nucleotides present in the pore can affect the pore'selectrical resistance, so current measurements over time can indicatethe sequence of DNA bases passing through the pore. This electricalcurrent signal (the ‘squiggle’ due to its appearance when plotted) isthe raw data gathered by a sequencer. These measurements can be storedas 16-bit integer data acquisition (DAC) values, taken at 4 kHzfrequency (for example). With a DNA strand velocity of ˜450 base pairsper second, this can give approximately nine raw observations per baseon average. This signal can then be processed to identify breaks in theopen pore signal corresponding to individual reads. These stretches ofraw signal can be base called—the process of converting DAC values intoa sequence of DNA bases. In some implementations, the sequencing datacan comprise normalized or scaled DAC values.

Current sequencing data 112 includes sequencing data generated by thesequencing data 104 for a current sequencing cycle of a sequencing run.In FIG. 1 , the current sequencing cycle is identified as the “N”sequencing cycle.

Previous sequencing data 116 includes sequencing data generated by thesequencing data 104 for one or more previous sequencing cycles of thesequencing run. The previous sequencing cycles precede the currentsequencing cycle. In FIG. 1 , the previous sequencing cycles areidentified as the “i to N−1” sequencing cycles. In otherimplementations, the previous sequencing data 116 includes sequencingdata for a subset of the 1 to N−1 sequencing cycles.

State generator 126 uses the current sequencing data 112 and theprevious sequencing data 116 to generate current state data 136 for thecurrent sequencing cycle. The state generator 126 can be a value orfunction that is applied to the sequencing data to generate a desiredresult. The state generator 126 can be applied to the sequencing data byany of a variety of mathematical manipulations including, but notlimited to addition, subtraction, division, multiplication, or acombination thereof. The state generator 126 can be a mathematicalformula, logic function, computer implemented algorithm, or the like.The sequencing data can be image data, electrical data, or a combinationthereof.

In one implementation, the state generator 126 generates the currentstate data 136 by accumulating summary statistics of the currentsequencing data 112 and the previous sequencing data 116. Examples ofthe summary statistics include maximum value, minimum value, average(mean), exponential weighted average, moving (running) average,exponential moving average, mode, standard deviation, variance,skewness, kurtosis, percentiles, and entropy. In other implementations,the state generator 126 determines secondary statistics based on thesummary statistics. Examples of the secondary statistics include deltas,sums, series of maximum values, series of minimum values, minimum of themaximum values in the series, and maximum of minimum values in theseries.

The base caller 144 generates current base call data 154 for the currentsequencing cycle in response to processing the current sequencing data112 and the current state data 136. The current base call data 154 caninclude base calls for one or more clusters. In some implementations,the current sequencing data 112 and the current state data 136 arecombined prior to processing by the base caller 144. The combination canbe brought about by, for example, summing operations, element-wisemultiplication operations, element-wise multiplication and summation(convolution) operations, and concatenation operations.

Examples of the base caller 144 include different base callingprocedures available for the Illumina platform, such as Real-TimeAnalysis (RTA), BlindCall, freelbis, Softy, AYB, OnlineCall, BM-BC,ParticleCall, TotalReCaller, naiveBayesCall, Srfim, BayesCall, Ibis,Rolexa, Alta-Cyclic, and Bustard. Examples of the base caller 144 alsoinclude Illumina's neural network-based offerings, such as the onesdisclosed in commonly owned patent application Ser. Nos. 16/825,987;16/825,991; 16/826,126; 16/826,134; 16/826,134; 16/826,168; 17/175,546;17/180,542; 17/176,151; 63/072,032; 63/161,880; 63/161,896; 17/176,147;63/228,954; 17/179,395; 17/180,480; 17/180,513; 63/169,163; and63/217,644, collectively referred to herein as “DeepRTA” or “DeepLearning Primary Analysis.” Yet other examples of the base caller 144include different base calling procedures available for the OxfordNanopore Technologies (ONT), such as Metrichor, Nanocall, DeepNano,Nanonet, Scrappie, Albacore, Guppy, Basecrawller, Chiron, Halcyon,MinCall, SACall, Causalcall, and WaveNano.

Pixelated State Data

FIG. 2 illustrates one implementation of executing a base callingoperation using state information 200 generated based on historicintensity values of pixels in the sequencing images.

At action 202, a memory stores (e.g., in the datastore 112), for acurrent sequencing cycle of a sequencing run, respective currentintensity values for respective pixels in a plurality of pixels. Ataction 212, the memory stores (e.g., in the datastore 116), for one ormore previous sequencing cycles of the sequencing run that precede thecurrent sequencing cycle, sequences of respective previous intensityvalues for the respective pixels. In one implementation, respectiveintensity values for the respective pixels are each characterized bychannel-specific intensity values for a plurality of channels. In oneimplementation, wherein the channels correspond to a combination ofillumination with a specific laser and imaging through a specificoptical filter. In another implementation, the channels correspond tofilter wavelength bands. In yet another implementation, the channelscorrespond to imaging events at a sequencing cycle.

At action 222, the state generator 126, having access to the memory,generates respective current state values for the respective pixels independence upon (i) the respective current intensity values, and (ii)respective previous intensity values in the sequences of respectiveprevious intensity values. Also, the state generator 126 stores therespective current state values in the memory. In some implementations,respective state values for the respective pixels are each characterizedby channel-specific state values for the plurality of channels. In oneimplementation, the respective current state values are pixel-wiseencoded with the respective current intensity values. In someimplementations, channel-specific state values for a subset of channelsin the plurality of channels are pixel-wise encoded with the respectivecurrent intensity values. In some implementations, the channel-specificstate values are averaged across channels in the plurality of channelsto generate respective pan-channel current state values. In oneimplementation, the respective pan-channel current state values arepixel-wise encoded with the respective current intensity value.

In some implementations, the pixel-wise encoding includes pixel-wiseconcatenation. In other implementations, the pixel-wise encodingincludes pixel-wise summing. In yet other implementations, thepixel-wise encoding includes element-wise multiplication. In yet otherimplementations, the pixel-wise encoding includes element-wisemultiplication and summation (convolution).

In one implementation, the respective state values are configured tocharacterize historic intensity patterns of the respective pixels. Inanother implementation, the historic intensity patterns are configuredto compensate for losses in base calling accuracy of the base caller144. In yet another implementation, the historic intensity patterns areconfigured to compensate for losses in base calling accuracy of the basecaller 144 when base calling bases for k-mers. In yet anotherimplementation, the respective state values are configured to discernrespective signal profiles of the clusters.

In one implementation, the respective current state values arerespective current average intensities determined for the respectivepixels at the current sequencing cycle from (i) the respective previousintensity values, and (ii) the respective current intensity values. Insome implementations, the respective current average intensities areeach characterized by channel-specific current average intensities.

In another implementation, the respective current state values arerespective current maximum intensities determined for the respectivepixels at the current sequencing cycle from (i) the respective previousintensity values, and (ii) the respective current intensity values. Insome implementations, the respective current maximum intensities areeach characterized by channel-specific current maximum intensities.

In yet another implementation, the respective current state values arerespective current maximum intensities determined for the respectivepixels at the current sequencing cycle from (i) the respective previousintensity values, and (ii) the respective current intensity values. Insome implementations, the respective current maximum intensities areeach characterized by channel-specific current maximum intensities.

In yet another implementation, the respective current state values arerespective current minimum intensities determined for the respectivepixels at the current sequencing cycle from (i) the respective previousintensity values, and (ii) the respective current intensity values. Insome implementations, the respective current minimum intensities areeach characterized by channel-specific current minimum intensities.

In yet another implementation, the respective current state values arerespective current exponentially weighted average intensities determinedfor the respective pixels at the current sequencing cycle from (i) therespective previous intensity values, and (ii) the respective currentintensity values. In some implementations, the respective currentexponentially weighted average intensities are determined based onweighting recent sequencing cycles more than earlier sequencing cycles.In other implementations, the respective current exponentially weightedaverage intensities are each characterized by channel-specific currentexponentially weighted average intensities.

In yet another implementation, the respective current state values arerespective current moving average intensities determined for therespective pixels at the current sequencing cycle from (i) therespective current intensity values, and (ii) a rolling subset of therespective previous intensity values. In some implementations, therespective current moving average intensities are each characterized bychannel-specific current moving average intensities.

In some implementations, each of the respective current intensity valuesis attributed to an active state bucket or an inactive state bucketbased on comparison of the respective current intensity values torespective current active state intensities and respective currentinactive state intensities. In one implementation, the respectivecurrent active state intensities are respective current global maximumintensities determined for the respective pixels at the currentsequencing cycle from (i) the respective previous intensity values. Insome implementations, the respective current global maximum intensitiesare each characterized by channel-specific current global maximumintensities.

In one implementation, the respective current inactive state intensitiesare respective current global minimum intensities determined for therespective pixels at the current sequencing cycle from (i) therespective previous intensity values. In some implementations, therespective current global minimum intensities are each characterized bychannel-specific current global minimum intensities.

In one implementation, the respective current state values furtherinclude respective current active state values and respective currentinactive state values generated for the respective pixels at the currentsequencing cycle. In some implementations, the respective current activestate values are each characterized by channel-specific current activestate values. In other implementations, the respective current inactivestate values are each characterized by channel-specific current inactivestate values.

In one implementation, a current active state value for a pixel in asubject channel is determined at the current sequencing cycle from (i) acurrent intensity value for the pixel in the subject channel that isdetected and attributed to the active state bucket at the currentsequencing cycle, and (ii) previous intensity values for the pixel inthe subject channel that are detected at the previous sequencing cycles.

In some implementations, the current intensity value is attributed tothe active state bucket based on comparison of the current intensityvalue to a global maximum value and a global minimum value determinedfrom the previous intensity values in the subject channel. In otherimplementations, the current intensity value is attributed to the activestate bucket based on comparison of the current intensity value to aprevious active state value and a previous inactive state valuedetermined at a preceding sequencing cycle in the subject channel. Inone implementation, the current active state value is an exponentiallyweighted average determined from the current intensity value and theprevious intensity values. In another implementation, the current activestate value is an average determined from the current intensity valueand the previous intensity values. In yet another implementation, thecurrent active state value is a moving average determined from thecurrent intensity value and a rolling subset of the previous intensityvalues. In yet another implementation, the current active state value isa minimum value determined from the current intensity value and theprevious intensity values. In yet another implementation, the currentactive state value is a maximum value determined from the currentintensity value and the previous intensity values.

In one implementation, the current active state value is carried fromthe preceding sequencing cycle and not redetermined at the currentsequencing cycle when the current intensity value is attributed to theinactive state bucket at the current sequencing cycle.

In one implementation, a current inactive state value for the pixel inthe subject channel is determined at the current sequencing cycle from(i) a current intensity value for the pixel in the subject channel thatis detected and attributed to the inactive state bucket at the currentsequencing cycle, and (ii) previous intensity values for the pixel inthe subject channel that are detected at the previous sequencing cycles.

In some implementations, the current intensity value is attributed tothe inactive state bucket based on comparison of the current intensityvalue to a global maximum value and a global minimum value determinedfrom the previous intensity values in the subject channel. In otherimplementations, the current intensity value is attributed to theinactive state bucket based on comparison of the current intensity valueto a previous inactive state value and a previous inactive state valuedetermined at a preceding sequencing cycle in the subject channel.

In some implementations, the current intensity value is attributed tothe inactive state bucket when the current intensity value is closer tothe global minimum value than to the global maximum value. In someimplementations, the current intensity value is attributed to the activestate bucket when the current intensity value is closer to the globalmaximum value than to the global minimum value.

In one implementation, the current inactive state value is anexponentially weighted average determined from the current intensityvalue and the previous intensity values. In another implementation, thecurrent inactive state value is an average determined from the currentintensity value and the previous intensity values. In yet anotherimplementation, the current inactive state value is a moving averagedetermined from the current intensity value and a rolling subset of theprevious intensity values. In yet another implementation, the currentinactive state value is a minimum value determined from the currentintensity value and the previous intensity values. In yet anotherimplementation, the current inactive state value is a maximum valuedetermined from the current intensity value and the previous intensityvalues.

In some implementations, the current inactive state value is carriedfrom the preceding sequencing cycle and not redetermined at the currentsequencing cycle when the current intensity value is attributed to theactive state bucket at the current sequencing cycle.

At action 232, the base caller 144, having access to the memory,generates base calls for the current sequencing cycle in response toprocessing (i) the respective current intensity values, and (ii) therespective current state values. In some implementations, the base callsfor the current sequencing cycle include base calls for one or moreclusters for which the respective current signal values and therespective previous signal values are detected.

In one implementation, the base caller 144 is a neural network. In someimplementations, the neural network is a convolutional neural network.In one implementation, the convolutional neural network includes aplurality of spatial convolution layers and a plurality of temporalconvolution layers.

In some implementations, the base caller 144 is trained using sequencingimages generated offline for an already-executed sequencing run. In oneimplementation, the respective state values for the respective pixelsare calculated offline for each sequencing cycle of the already-executedsequencing run in advance of base calling by the base caller 144. In oneimplementation, the base caller 144 is trained to use the respectivestate values to compensate for losses in base calling accuracy.

In some implementations, the respective current state values areiteratively generated at each sequencing cycle of the sequencing run. Insome implementations, the memory is further configured to store, fornext sequencing cycles of the sequencing run, sequences of respectivenext intensity values for the respective pixels. In someimplementations, the base caller 144 is further configured to generatethe base calls for the current sequencing cycle in response toprocessing (i) the respective current intensity values, (ii) respectiveprevious intensity values in the sequences of respective previousintensity values for one or more of the previous sequencing cycles,(iii) respective next intensity values in the sequences of respectivenext intensity values for one or more of the next sequencing cycles, and(iv) the respective state values. In some implementations, therespective state values are pixel-wise encoded with respective previousintensity values and the respective next intensity values.

Per-Pixel States

FIG. 3 illustrates one implementation of generating per-pixel states 300for pixels in sequencing images processed as input for base calling.FIG. 3 depicts example pixel grids 302N, 302N−1, . . . , 302 of sizes3×3. FIG. 3 also shows a per-pixel state grid 304. The pixel grids 302N,302N−1, . . . , 302 represent sequencing images that are provided asinput to the base caller 144 for base calling. Pixels in the pixel grids302N, 302N−1, . . . , 302 can have intensity values for a plurality ofimage channels, for example, a red channel and a blue channel. Pixelgrid 302N is generated for a current sequencing cycle N of a sequencingrun and depicts intensity emissions generated as a result of nucleotideincorporation in a set of clusters at the current sequencing cycle N.Similarly, pixel grid 302N−1 is generated for a previous sequencingcycle N−1 of the sequencing run and depicts intensity emissionsgenerated as a result of nucleotide incorporation in the set of clustersat the previous sequencing cycle N−1. Likewise, pixel grid 302 isgenerated for a first sequencing cycle 1 of the sequencing run anddepicts intensity emissions generated as a result of nucleotideincorporation in the set of clusters at the first sequencing cycle 1.

From a spatial perspective, the pixel grids 302N, 302N−1, . . . , 302can be considered to share nine pixels 1-9. In FIG. 3 , the nine pixels1-9 are indexed by same per-pixel superscripts across the pixel grids302N, 302N−1, . . . , 302. From a temporal perspective, the nine pixels1-9 can be considered to depict a first set of intensity values at acurrent timestep N, a second set of intensity values at a previoustimestep N−1, and a third set of intensity values at a first timestep 1.Intensity values in the first, second, and third sets of intensityvalues can be considered to be arranged in a temporal sequence. In FIG.3 , the time-varying intensity values are indexed by different per-pixelsuperscripts across the pixel grids 302N, 302N−1, . . . , 302.

A current pixel state 318 for a given pixel can be calculated at thecurrent sequencing N based on a current intensity value 312 of the givenpixel at the current sequencing N and previous intensity values 314, . .. , 316 of the given pixel at the respective previous sequencing cyclesN−1 to 1. The previous intensity values 314, . . . , 316 of the givenpixel can be considered to be arranged in a previous intensity sequencestarting at a previous intensity position N−1 and terminating at aprevious intensity position 1.

In one implementation, the current pixel state 318 is the output of astate generation logic (or function) that takes as input the currentintensity value 312 and the previous intensity values 314, . . . , 316.In one implementation, the state generation logic can select a maximumvalue from among the current intensity value 312 and the previousintensity values 314, . . . , 316, and use the maximum value as thecurrent pixel state 318. In another implementation, the state generationlogic can select a minimum value from among the current intensity value312 and the previous intensity values 314, . . . , 316, and use theminimum value as the current pixel state 318. In yet anotherimplementation, the state generation logic can determine an average(mean) of the current intensity value 312 and the previous intensityvalues 314, . . . , 316, and use the average as the current pixel state318. In yet another implementation, the state generation logic candetermine an exponential weighted average of the current intensity value312 and the previous intensity values 314, . . . , 316, and use theexponential weighted average as the current pixel state 318. In yetanother implementation, the state generation logic can determine amoving (running) average of the current intensity value 312 and theprevious intensity values 314, . . . , 316, and use the moving averageas the current pixel state 318. In yet another implementation, the stategeneration logic can determine an exponential moving average of thecurrent intensity value 312 and the previous intensity values 314, . . ., 316, and use the exponential moving average as the current pixel state318. In yet another implementation, the state generation logic candetermine a standard deviation of the current intensity value 312 andthe previous intensity values 314, . . . , 316, and use the standarddeviation as the current pixel state 318. This process is implementedfor each of the nine pixels 1-9 such that a respective current pixelstate is determined for each of the nine pixels 1-9. Current pixelstates generated by the state generation logic for the nine pixels 1-9form the per-pixel state grid 304, according to the example illustratedin FIG. 3 . In one implementation, the state generation logic isimplemented by the state generator 126.

Pixel-Wise State Encoding

FIG. 4 illustrates one implementation of generating base calls based onthe per-pixel states 304. In one implementation, an encoding logic 402pixel-wise combines 400 the pixel grid 302N and the per-pixel state grid304, for example, by summation, concatenation, element-wisemultiplication, or element-wise multiplication and summation(convolution). The combination of the pixel grid 302N and the per-pixelstate grid 304 is processed by the base caller 144 to generate thecurrent base call data 154.

Per-Channel States

FIG. 5 illustrates one implementation of generating per-channel states500 for pixels in sequencing images processed as input for base calling.FIG. 5 relates to an example pixel P. In FIG. 5 , pixel P has intensityvalues across two example channels (or intensity channels): channel 1502 and channel 2 506. In other implementations, pixel P can have feweror greater number of channels. In the example illustrated in FIG. 5 ,the first channel 502 has a first set of intensity values 512, 513, . .. , 516 for five sequencing cycles 1-5. Likewise, the second channel 506has a second set of intensity values 522, 523, . . . , 526 for the fivesequencing cycles 1-5.

Then, a first channel state 532 is determined for the first channel 502based on the first set of intensity values. Similarly, a second channelstate 536 is determined for the second channel 506 based on the secondset of intensity values. The first and second channel states 532 and 536can be determined by the state generation logic, for example, byimplementing a minimum value selection function, a maximum valueselection function, an average function, an exponential weightingaverage function, a moving average function, an exponential movingaverage function, or a standard deviation function.

Then, at least one base call is generated for the fifth sequencing cycle(Cycle 5) based on the first channel state 532 and the second channelstate 536. This includes, in one implementation, combining the channel 1intensity value 516 for Cycle 5 with the first channel state 532 togenerate a first combination for channel 1 502, and combining thechannel 2 intensity value 526 for Cycle 5 with the second channel state536 to generate a second combination for channel 2 506. Then, the basecaller 144 processes the first combination for channel 1 502 and thesecond combination for channel 2 506 to generate one or more base callsfor Cycle 5 (e.g., for one or more clusters).

In another implementation, the base caller 144 processes the channel 1intensity value 516 for Cycle 5, the channel 2 intensity value 526 forCycle 5, the first channel state 532 for Cycle 5, and the second channelstate 536 for Cycle 5 as four separate input channels (e.g., RGB-styleimage channels) and generates the base call for Cycle 5. A personskilled in the art would appreciate that other contemporary or futuremethods of combining data/channels and processing them as a combinationcan be analogously applied and equivalently used within the scope ofthis disclosure.

In yet another implementation, the first and second channel states 532and 536 are combined to generate a so-called “pan-channel state,” forexample, by applying an averaging function on the first and secondchannel states 532 and 536. Then, this pan-channel state for Cycle 5 isprocessed along with the channel 1 intensity value 516 for Cycle 5 andthe channel 2 intensity value 526 for Cycle 5 to generate the base callfor Cycle 5, i.e., a total of three separate input channels as opposedto four in the per-channel implementation.

Even though FIG. 5 relates to only one pixel P for purposes ofillustration and simplicity, it is understood that the steps of stategeneration- and state encoding-responsive base calling are implementedfor multiple pixels in the input sequencing images on a pixel-by-pixelbasis and a channel-by-channel basis, partly because cluster intensityprofiles and their states are defined by groups of contiguous pixels andtherefore are analyzed as a group to generate a base call for a cluster.

Per-Channel MIN States

FIG. 6 illustrates one implementation of generating per-channel MIN(minimum) states 600 for pixels in sequencing images processed as inputfor base calling. In FIG. 6 , column 601 indexes a plurality of pixelsof an input sequencing image that is processed by the base caller 144for base calling. The input sequencing image represents intensityprofiles captured for a set of clusters at a current sequencing cycle Nof a sequencing run. In FIG. 6 , column 602 represents pixel-wiseintensity values in a first channel (image channel) for the pixelsindexed in the column 601 and for the current sequencing cycle N. InFIG. 6 , column 603 represents pixel-wise intensity values in a secondchannel (image channel) for the pixels indexed in the column 601 and forthe current sequencing cycle N.

In FIG. 6 , column 604 represents pixel-wise minimum intensity values inthe first channel selected from intensity values observed in the firstchannel between a first sequencing cycle 1 and the current sequencingcycle N for the pixels indexed in the column 601. In FIG. 6 , column 605represents pixel-wise minimum intensity values in the second channelselected from intensity values observed in the second channel betweenthe first sequencing cycle 1 and the current sequencing cycle N for thepixels indexed in the column 601.

In one implementation, the base caller 144 processes columns 602, 603,604, and 605 as separate input channels to generate one or more basecalls for the current sequencing cycle N.

Pan-Channel MIN States

FIG. 7 illustrates one implementation of generating pan-channel MIN(minimum) states 700 for pixels in sequencing images processed as inputfor base calling. In FIG. 7 , column 708 pixel-wise averages the columns604 and 605 of FIG. 6 . In one implementation, the base caller 144processes columns 602, 603, and 708 as separate input channels togenerate one or more base calls for the current sequencing cycle N.

Per-Channel MAX States

FIG. 8 illustrates one implementation of generating per-channel MAX(maximum) states 800 for pixels in sequencing images processed as inputfor base calling. In FIG. 8 , column 804 represents pixel-wise maximumintensity values in the first channel selected from intensity valuesobserved in the first channel between the first sequencing cycle 1 andthe current sequencing cycle N for the pixels indexed in the column 601.In FIG. 8 , column 805 represents pixel-wise maximum intensity values inthe second channel selected from intensity values observed in the secondchannel between the first sequencing cycle 1 and the current sequencingcycle N for the pixels indexed in the column 601.

In one implementation, the base caller 144 processes columns 602, 603,804, and 805 as separate input channels to generate one or more basecalls for the current sequencing cycle N.

Pan-Channel MAX States

FIG. 9 illustrates one implementation of generating pan-channel MAX(maximum) states 900 for pixels in sequencing images processed as inputfor base calling. In FIG. 9 , column 908 pixel-wise averages the columns804 and 805 of FIG. 8 . In one implementation, the base caller 144processes columns 602, 603, and 908 as separate input channels togenerate one or more base calls for the current sequencing cycle N.

Per-Channel AVG States

FIG. 10 illustrates one implementation of generating per-channel AVG(average) states 1000 for pixels in sequencing images processed as inputfor base calling. In FIG. 10 , column 1004 represents pixel-wise averageintensity values in the first channel calculated as respective per-pixelmeans of intensity values observed in the first channel between thefirst sequencing cycle 1 and the current sequencing cycle N for thepixels indexed in the column 601. In FIG. 10 , column 1005 representspixel-wise average intensity values in the second channel calculated asrespective per-pixel means of intensity values observed in the secondchannel between the first sequencing cycle 1 and the current sequencingcycle N for the pixels indexed in the column 601.

In one implementation, the base caller 144 processes columns 602, 603,1004, and 1005 as separate input channels to generate one or more basecalls for the current sequencing cycle N.

Pan-Channel AVG States

FIG. 11 illustrates one implementation of generating pan-channel AVG(average) states 1100 for pixels in sequencing images processed as inputfor base calling. In FIG. 11 , column 1108 pixel-wise averages thecolumns 1004 and 1005 of FIG. 10 . In one implementation, the basecaller 144 processes columns 602, 603, and 1108 as separate inputchannels to generate one or more base calls for the current sequencingcycle N.

Per-Channel MIN MAX States

FIG. 12 illustrates one implementation of generating per-channel MIN MAX(minimum and maximum) states 1200 for pixels in sequencing imagesprocessed as input for base calling. In one implementation, the basecaller 144 processes columns 602, 603, 604, 605, 804, and 805 asseparate input channels to generate one or more base calls for thecurrent sequencing cycle N.

Per-Channel MIN AVG States

FIG. 13 illustrates one implementation of generating per-channel MIN AVG(minimum and average) states 1300 for pixels in sequencing imagesprocessed as input for base calling. In one implementation, the basecaller 144 processes columns 602, 603, 604, 605, 1004, and 1005 asseparate input channels to generate one or more base calls for thecurrent sequencing cycle N.

Per-Channel MAX AVG States

FIG. 14 illustrates one implementation of generating per-channel MAX AVG(maximum and average) states 1400 for pixels in sequencing imagesprocessed as input for base calling. In one implementation, the basecaller 144 processes columns 602, 603, 804, 805, 1004, and 1005 asseparate input channels to generate one or more base calls for thecurrent sequencing cycle N.

Per-Channel MIN MAX AVG States

FIG. 15 illustrates one implementation of generating per-channel MIN MAXAVG (minimum, maximum, and average) states 1500 for pixels in sequencingimages processed as input for base calling. In one implementation, thebase caller 144 processes columns 602, 603, 604, 605, 804, 805, 1004,and 1005 as separate input channels to generate one or more base callsfor the current sequencing cycle N.

A person skilled in the art will appreciate that any combination orordering or arrangement of the channels and states discussed above canbe provided as input to the base caller 144 for base calling. Othercontemporaneous and future ways of combining, ordering, and arrangingthe channels and states discussed above are within the scope of thisdisclosure. For example, the pan-channel states can be generated bycombining the per-channel states using other aggregation/accumulationfunctions like exponential weighted average, moving average, standarddeviation, variance, and so on. In another example, the per-channel MINstates can be concatenated with the pan-channel AVG MAX states, and soon.

Base Call-Directed State Generation

FIG. 16 illustrates one implementation of generating 1600 stateinformation in dependence upon previously called bases for use in futurebase calling. FIGS. 17A, 17B, and 17C illustrate base call-directed ONand OFF state generation 1700 for a current sequencing cycle eleven(Cycle 11). FIGS. 18A, 18B, and 18C expand on FIGS. 17A, 17B, and 17Cand illustrate how the OFF state is updated 1800 at a next sequencingcycle twelve (Cycle 12). FIGS. 19A, 19B, and 19C further expand on FIGS.18A, 18B, and 18C and illustrate how the ON state is updated 1900 at ayet next sequencing cycle thirteen (Cycle 13). FIGS. 20A, 20B, and 20Cfurther expand on FIGS. 19A, 19B, and 19C and illustrate how both the ONand OFF states are updated 2000 at a yet next sequencing cycle fourteen(Cycle 14).

One avenue of differentiating between the different strategies fordetecting nucleotide incorporation in a sequencing reaction using onefluorescent dye (or two or more dyes of same or similarexcitation/emission spectra) is by characterizing the incorporations interms of the presence or relative absence, or levels in between, offluorescence transition that occurs during a sequencing cycle. As such,sequencing strategies can be exemplified by their fluorescent profilefor a sequencing cycle. For strategies disclosed herein, “1” and “0”denotes a fluorescent state in which a nucleotide is in a signal state(e.g., detectable by fluorescence) (1)/(ON) or whether a nucleotide isin a dark state (e.g., not detected or minimally detected at an imagingstep) (0)/OFF. A “0,” “OFF,” or “dark” state does not necessarily referto a total lack, or absence of signal. Although in some implementationsthere may be a total lack or absence of signal (e.g., fluorescence),while in other implementation there may be some detectable signal evenin the OFF state. Minimal or diminished fluorescence signal (e.g.,background signal) is also contemplated to be included in the scope of a“0,” “OFF,” or “dark” state as long as a change in fluorescence from thefirst to the second image (or vice versa) can be reliably distinguished.

As used herein, the term “dark” or “OFF” is intended to refer to anamount of desired signal detected by a detector that is insignificantcompared to background signal detected by the detector. For example, afeature of an object may be considered to be dark or OFF when the signalto noise ratio for the feature is substantially low, for example, beingless than 1. In some implementations, a dark or OFF feature may notproduce any amount of a desired signal (i.e., no signal is produced ordetected). In other implementations, a very low amount of signal,relative to background, may be considered as dark or OFF.

In one implementation, an exemplary strategy for detecting anddetermining nucleotide incorporation in a sequencing reaction using onefluorescent dye (or two dyes of same or similar excitation/emissionspectra) and two imaging events is exemplified by the followingdetection table:

Image 1 Image 2 A ON OFF C OFF ON G OFF OFF T ON ON

Other strategies for detecting nucleotide incorporation, such as thefour-channel chemistry and the one-channel chemistry, are within thescope of this disclosure and not discussed separately.

At action 1602, the technology disclosed accesses, for a given pixel,channel-wise active (ON) and inactive (OFF) classifications underlyingbases called in previous sequencing cycles, i.e., previously calledbases. This is illustrated by example in FIG. 17A. In FIG. 17A, pixel Pdepicts intensity emissions of a corresponding cluster for which basesare called for Cycles 1 to 10. In some implementations, pixel P containsa center of the corresponding cluster, as determined by locationcoordinates of the corresponding cluster's center. The called basesemanate from channel-wise active (ON) and inactive (OFF) classifications1754 and 1756 for intensity values in a first channel (channel 1) andfor intensity values in a second channel (channel 2).

At action 1612, the technology disclosed accumulates, for the givenpixel and a current sequencing cycle, channel-wise summary statisticsfor per-channel active (ON) and inactive (OFF) states based on thechannel-wise active (ON) and inactive (OFF) classifications 1754 and1756. This is illustrated by example in FIG. 17B. In FIG. 17B, aninactive (OFF) state for the first channel 1702 is determined for thesequencing cycle eleven (Cycle 11) by applying an accumulation functionto those intensity values in column 1754 that are classified to theinactive (OFF) state. Examples of the accumulation function includemaximum value selection, minimum value selection, average (mean),exponential weighted average, moving (running) average, exponentialmoving average, mode, standard deviation, variance, skewness, kurtosis,percentiles, and entropy. In FIG. 17B, an active (ON) state for thefirst channel 1712 is determined for Cycle 11 by applying theaccumulation function to those intensity values in column 1754 that areclassified to the active (ON) state. In FIG. 17B, an inactive (OFF)state for the second channel 1722 is determined for Cycle 11 by applyingthe accumulation function to those intensity values in column 1756 thatare classified to the inactive (OFF) state. In FIG. 17B, an active (ON)state for the second channel 1732 is determined for Cycle 11 by applyingthe accumulation function to those intensity values in column 1756 thatare classified to the active (ON) state.

At action 1622, the technology disclosed combines, for the given pixeland the current sequencing cycle, the accumulated channel-wise summarystatistics for the per-channel active (ON) states and inactive (OFF)states with current intensity channels of the given pixel. This isillustrated by example in FIG. 17C. In FIG. 17C, 1764 and 1766 arerespective intensity values for the first and second channels that areregistered for pixel P at Cycle 11. In FIG. 17C, the base caller 144processes 1764, 1766, 1702, 1712, 1722, and 1732 as separate inputchannels to generate abase call for the corresponding cluster of pixel Pat the sequencing cycle 11.

At action 1632, the technology disclosed generates, for the currentsequencing cycle, at least one base call based on the combination of theaccumulated channel-wise summary statistics for the per-channel active(ON) states and inactive (OFF) states and the intensity channels of thegiven pixel. This is illustrated by example in FIG. 18A. In FIG. 18A, abase call “G” is generated for the corresponding cluster of pixel P atCycle 11 based on “OFF” and “OFF” channel-wise classifications 1854 and1856, which in turn are made by the base caller 144 in response toprocessing 1764, 1766, 1702, 1712, 1722, and 1732 in FIG. 18C.

In FIG. 18B, in response to the “OFF” and “OFF” channel-wiseclassifications 1854 and 1856 at Cycle 11 (and lack of any “ON”classification), only the OFF channel states for the first and secondchannels 1802 and 1822 are updated at Cycle 12, such that theaccumulation function is reapplied to now also consider channel-wiseintensity values of Cycle 11 that both have “OFF” classifications 1854and 1856. In some implementations, the channel-wise ON and OFF statesare maintained in memory as single variables and reverse calculated atevery update instance, for example, by multiplication with the divisorthat was used to calculate the average when the accumulation functionwas an averaging function. The ON channel states for the first andsecond channels 1812 and 1832 are kept unchanged at Cycle 12 and inheritthe same values from Cycle 11, i.e., 1712 and 1732, respectively.

In FIG. 18C, 1864 and 1866 are respective intensity values for the firstand second channels that are registered for pixel P at Cycle 12. In FIG.18C, the base caller 144 processes 1864, 1866, 1802, 1812, 1822, and1832 as separate input channels to generate a base call for thecorresponding cluster of pixel P at the sequencing cycle 12.

In FIG. 19A, a base call “T” is generated for the corresponding clusterof pixel P at Cycle 12 based on “ON” and “ON” channel-wiseclassifications 1954 and 1956, which in turn are made by the base caller144 in response to processing 1864, 1866, 1802, 1812, 1822, and 1832 inFIG. 18C.

In FIG. 19B, in response to the “ON” and “ON” channel-wiseclassifications 1954 and 1956 at Cycle 12 (and lack of any “OFF”classification), only the ON channel states for the first and secondchannels 1912 and 1932 are updated at Cycle 13, such that theaccumulation function is reapplied to now also consider channel-wiseintensity values of Cycle 12 that both have “ON” classifications 1954and 1956. The OFF channel states for the first and second channels 1902and 1922 are kept unchanged at Cycle 13 and inherit the same values fromCycle 12, i.e., 1802 and 1822, respectively.

In FIG. 19C, 1964 and 1966 are respective intensity values for the firstand second channels that are registered for pixel P at Cycle 13. In FIG.19C, the base caller 144 processes 1964, 1966, 1902, 1912, 1922, and1932 as separate input channels to generate a base call for thecorresponding cluster of pixel P at the sequencing cycle 13.

In FIG. 20A, a base call “A” is generated for the corresponding clusterof pixel P at Cycle 13 based on “ON” and “OF” channel-wiseclassifications 2054 and 2056, which in turn are made by the base caller144 in response to processing 1964, 1966, 1902, 1912, 1922, and 1932 inFIG. 19C.

In FIG. 20B, in response to the “ON” and “OFF” channel-wiseclassifications 2054 and 2056 at Cycle 13, both the ON and OFF channelstates for the first and second channels 2002, 2012, 2022, and 2032,respectively, are updated at Cycle 14, such that the accumulationfunction is reapplied to now also consider channel-wise intensity valuesof Cycle 13 that have “ON” and “OFF” classifications 2054 and 2056.

In FIG. 20C, 2064 and 2066 are respective intensity values for the firstand second channels that are registered for pixel P at Cycle 14. In FIG.20C, the base caller 144 processes 2064, 2066, 2002, 2012, 2022, and2032 as separate input channels to generate a base call for thecorresponding cluster of pixel P at the sequencing cycle 14.

Note that with base call-directed state generation, the stateinformation lags by one sequencing cycle. That is, the state information1802, 1812, 1822, and 1832 used to base call Cycle 12 is based on Cycles1-11 and does not include Cycle 12. Similarly, the state information1902, 1912, 1922, and 1932 used to base call Cycle 13 is based on Cycles1-12 and does not include Cycle 13.

Exponential Weighted Averaging

FIG. 21 illustrates one implementation of generating 2100 stateinformation for base calling using exponential weighted averaging. Inone implementation, exponential weighted averaging is used to give moreweight to input signals (e.g., intensity values) from more recentsequencing cycles when calculating the state information. As a result,the state information estimates generated by exponential weightedaveraging follow changes in the signals and are more robust to outliers.

At action 2102, the technology disclosed accumulates, for a given pixel,summary statistics for historic intensity values from previoussequencing cycles (e.g., the first ten or twenty sequencing cycles).Examples of an accumulation function that accumulates the summarystatistics include maximum value selection, minimum value selection,average (mean), exponential weighted average, moving (running) average,exponential moving average, mode, standard deviation, variance,skewness, kurtosis, percentiles, and entropy.

At action 2112, the technology disclosed initializes, for the givenpixel, a starting exponential weighted average based on the accumulatedsummary statistics. In one implementation, the starting exponentialweighted average can be an average of the historic intensity values(e.g., an average of the historical intensity values of all other pixelsor all other cluster pixels), a global maximum (global MAX) valueselected from the historic intensity values (e.g., a global MAX of thehistorical intensity values of all other pixels or all other clusterpixels), or a global minimum (global MIN) selected from the historicintensity values (e.g., a global MIN of the historical intensity valuesof all other pixels or all other cluster pixels).

At action 2122, the technology disclosed determines, for the given pixeland a current sequencing cycle, a current exponential weighted averagebased on a weighted combination of the starting exponential weightedaverage and a current pixel intensity value.

At action 2132, the technology disclosed uses the current exponentialweighted average as current state value for base calling at the currentsequencing cycle (e.g., base calling a cluster corresponding to thegiven pixel). In one implementation, this includes the base caller 144processing the current state value and the current pixel intensity valueand generating at least one base call for the current sequencing cycle.

At action 2142, the technology disclosed determines, for the given pixeland a next sequencing cycle, a next exponential weighted average basedon a weighted combination of the current exponential weighted averageand a next pixel intensity value.

At action 2152, the technology disclosed uses the next exponentialweighted average as next state value for base calling at the nextsequencing cycle (e.g., base calling the cluster corresponding to thegiven pixel). In one implementation, this includes the base caller 144processing the next state value and the next pixel intensity value andgenerating at least one base call for the next sequencing cycle.

In one implementation, the exponential weighted average logic isexpressed as following:

y[k]=(1−alpha)*y[k−1]+alpha*x[k]

where

-   -   y[k] is the exponential weighted average for the current        sequencing cycle    -   y[k−1] is the exponential weighted average for the previous        sequencing cycle    -   x[k] is the input signal (e.g., intensity value) for the current        sequencing cycle    -   alpha is the weighting parameter

The weighting parameter “alpha” can be a value between zero and one{0≤alpha≤1}. When alpha is equal to zero {alpha=0}, the output is y[k−1](no averaging). When alpha is equal to one {alpha=1}, the output isx[k]. At other alpha values, the output is an exponentially weightedaverage of the intensity. Alpha represents how quickly the filter reactsto updates; smaller values react slower and do more averaging, whereaslarger values put more weight on the more recent input values.

Note that y[.] can be stored as a single variable and updated in placesince historical values are not used except when updating theabove-mentioned expression. y[k−1] can be initialized to, for example,an expected intensity (e.g., an average initial intensity over all theclusters/wells). In one implementation, the first twenty sequencingcycles (Cycles 1-20) can be used to estimate an initial averageintensity with no online estimation. The initial average intensity canbe plugged in as the initial estimate for y[k−1], and then onlineexponential weighted averaging can be used after sequencing cycle twenty(Cycle 20).

For implementation on CPU, the update can be efficiently implementedusing one multiply by rewriting the above-mentioned expression as:

y[k]=y[k−1]*alpha (x[k]−y[k−1])

In some implementations, alpha can be a small value close to zero, somore averaging is performed. In other implementations, alpha can bevaried to find the value that trade-offs averaging v/s response timethat are most-suited for particular applications.

Per-Channel Exponential Weighted Averaging

FIG. 22 illustrates one implementation of generating 2200 per-channelstate information for base calling using exponential weighted averaging.FIG. 23 depicts an example of generating 2300 per-channel stateinformation for base calling using exponential weighted averaging.

At action 2202, the technology disclosed accumulates, for a given pixel,channel-wise summary statistics for per-channel historic intensityvalues from previous sequencing cycles (e.g., the first ten or twentysequencing cycles). Examples of a per-channel accumulation function thataccumulates the channel-wise summary statistics include maximum/globalmaximum value selection, minimum/global minimum value selection, average(mean), exponential weighted average, moving (running) average,exponential moving average, mode, standard deviation, variance,skewness, kurtosis, percentiles, and entropy. This is illustrated byexample in FIG. 23 .

In FIG. 23 , a first channel (channel 1) has historic intensity values2374 for the first five sequencing cycles (Cycles 1-5). In FIG. 23 , asecond channel (channel 2) has historic intensity values 2376 for Cycles1-5. Furthermore, a global maximum (global MAX) value 2312 is selectedfor channel 1 from the historic intensity values 2374, and a globalminimum (global MIN) value 2314 is selected for channel 1 from thehistoric intensity values 2374. Similarly, a global MAX value 2316 isselected for channel 2 from the historic intensity values 2376, and aglobal MIN value 2318 is selected for channel 2 from the historicintensity values 2376.

At action 2212, the technology disclosed, initializes, for each channel,a pair of starting active (ON) and inactive (OFF) states based on theaccumulated channel-wise summary statistics. This is illustrated byexample in FIG. 23 . In FIG. 23 , for channel 1 and after Cycle 5, theglobal MAX 2312 is initialized as the active (ON) state for channel 1,and the global MIN 2314 is initialized as the inactive (OFF) state forchannel 1. In FIG. 23 , for channel 2 and after Cycle 5, the global MAX2316 is initialized as the active (ON) state for channel 2, and theglobal MIN 2318 is initialized as the inactive (OFF) state for channel2.

At action 2222, the technology disclosed attributes, for the given pixeland a current sequencing cycle, on a channel-wise basis, current channelintensity values to the starting active (ON) states or the startinginactive (OFF) states based on comparison of the current channelintensity values to the pair of starting active (ON) and inactive (OFF)states. This is illustrated by example in FIG. 23 . In FIG. 23 , forchannel 1 and for Cycle 6, an intensity value 2322 is registered. Sinceintensity value 2322 is quantitatively closer to the global MAX 2312,the intensity value 2322 is attributed to the active (ON) state becausethe global MAX 2312 serves as the proxy for the active (ON) state forchannel 1 and is used as the starting exponential weighted average forchannel 1 in the active (ON) state. In FIG. 23 , for channel 2 and forCycle 6, an intensity value 2326 is registered.

Since intensity value 2326 is quantitatively closer to the global MIN2318, the intensity value 2326 is attributed to the inactive (OFF) statebecause the global MIN 2318 serves as the proxy for the inactive (OFF)state for channel 2 and is used as the starting exponential weightedaverage for channel 2 in the inactive (OFF) state.

At action 2232, the technology disclosed uses, for the given pixel andwithin a given channel, exponential weighting average to update anattributed state based on a weighted combination of a current channelintensity value and a previous state value of the attributed state.

At action 2242, the technology disclosed maintains, for the given pixeland within the given channel, an unattributed state from a previousstate value of the unattributed state.

Actions 2232 and 2242 are illustrated by example in FIG. 23 . In FIG. 23, for Cycle 6, the state information is determined for channels 1 and 2as follows. An active (ON) state 2342 for Cycle 6 and channel 1 isdetermined using an exponential weighted average 2332 because anintensity value 2322 for Cycle 6 and channel 1 is attributed to theactive (ON) state for channel 1. The exponential weighted average 2332uses “0.12” as the alpha value, as discussed with respect to theabove-mentioned expression. An inactive (OFF) state 2344 for channel 1is kept unchanged and is inherited from Cycle 5 as the global MIN 2314for channel 1.

An inactive (OFF) state 2348 for Cycle 6 and channel 2 is determinedusing an exponential weighted average 2338 because an intensity value2326 for Cycle 6 and channel 2 is attributed to the inactive (OFF) statefor channel 2. The exponential weighted average 2338 also uses “0.12” asthe alpha value, as discussed with respect to the above-mentionedexpression. An active (ON) state 2346 for channel 2 is kept unchangedand is inherited from Cycle 5 as the global MAX 2316 for channel 2.

Then, the channel-wise active (ON) and inactive (OFF) states 2342, 2344,2346, and 2348 for Cycle 6 are used along with the intensity values 2322and 2326 registered for Cycle 6 to generate at least one base call forCycle 6.

In FIG. 23 , for Cycle 7, the state information is determined forchannels 1 and 2 as follows. An inactive (OFF) state 2374 for Cycle 7and channel 1 is determined using an exponential weighted average 2364because an intensity value 2352 for Cycle 7 and channel 1 is attributedto the inactive (OFF) state for channel 1. The exponential weightedaverage 2364 also uses “0.12” as the alpha value, as discussed withrespect to the above-mentioned expression. An active (ON) state 2372 forchannel 1 is kept unchanged and is inherited from Cycle 6 as theexponential weighted average 2342 for channel 1.

An active (ON) state 2376 for Cycle 7 and channel 2 is determined usingan exponential weighted average 2366 because an intensity value 2356 forCycle 7 and channel 2 is attributed to the active (ON) state for channel2. The exponential weighted average 2366 also uses “0.12” as the alphavalue, as discussed in the above-mentioned expression. An inactive (OFF)state 2378 for channel 2 is kept unchanged and is inherited from Cycle 6as the exponential weighted average 2348 for channel 2.

Then, the channel-wise active (ON) and inactive (OFF) states 2372, 2374,2376, and 2378 for Cycle 7 are used along with the intensity values 2352and 2356 registered for Cycle 7 to generate at least one base call forCycle 7.

Note that, in FIG. 23 , the channel-wise attributions to active (ON) andinactive (OFF) states at a given sequencing cycle are based on theper-channel intensity values registered for the given sequencing cycle(e.g., Cycles 6 and 7 in FIG. 23 ). The discussion now turns to basecall-directed per-channel exponential weighted averaging in which thechannel-wise attributions to active (ON) and inactive (OFF) states atthe given sequencing cycle are based on a base call made a previoussequencing cycle.

Base Call-Directed Per-Channel Exponential Weighted Averaging

FIG. 24 illustrates one implementation of generating 2400 per-channelstate information for base calling using exponential weighted averagingthat is directed by previously called bases. FIG. 25 depicts an exampleof generating 2500 per-channel state information for base calling usingexponential weighted averaging that is directed by previously calledbases.

In FIG. 24 , actions 2402 and 2412 are similar to actions 2202 and 2212of FIG. 22 . At action 2422, the channel-wise attributions to active(ON) and inactive (OFF) states lag by one sequencing cycle and are madebased on a base called in a previous sequencing cycle. This isillustrated by example in FIG. 25 . In FIG. 25 , at Cycle 6, abase call“A” 2502 is made based on “ON” and “OFF” classifications 2504 and 2506of channels 1 and 2, respectively.

At action 2432, the “ON” and “OFF” classifications 2504 and 2506 and thebase call 2502 at Cycle 6 are used to determine the state information2342, 2344, 2346, and 2348 for Cycle 7 by applying the exponentialweighted averaging. At action 2442, unattributed states for Cycle 7 arealso identified based on the base call 2502 at Cycle 6 and keptunchanged.

Note that, at Cycle 7, “OFF” and “ON” classifications 2514 and 2516 andin turn the base call “C” 2512 are generated based on the stateinformation 2342, 2344, 2346, and 2348 determined at Cycle 6 andper-channel intensity values 2372 and 2376 registered at Cycle 7.

Continuing ahead, the “OFF” and “ON” classifications 2514 and 2516 andthe base call 2512 at Cycle 7 are used to determine the stateinformation 2372, 2374, 2376, and 2378 for Cycle 8 by applying theexponential weighted averaging. Unattributed states for Cycle 8 are alsoidentified based on the base call 2512 at Cycle 7 and kept unchanged.

Though not shown, a base call for Cycle 8 is generated based onchannel-wise intensity values registered for Cycle 8 and the stateinformation 2372, 2374, 2376, and 2378 determined at Cycle 7 for Cycle8.

In different implementations, state information determined usingexponential weighted averages (EWAs) can be used instead of, in additionto, or in combination with state information determined using some otherlogic, such as the minimum value selection logic, the maximum valueselection logic, the averaging logic, and so on.

Non-Cluster Pixels

The discussion now turns to the distinction between cluster pixels andnon-cluster pixels. Cluster pixels are pixels that contain centers ofclusters, as determined by location coordinates of cluster centers.Non-cluster pixels do not contain cluster centers. Note that non-clusterpixels do depict cluster intensities (or background intensities); theyjust do not coincide with cluster centers.

A brief discussion of cluster-to-pixel-to-base call relationship mayalso be helpful here. A cluster is depicted by a plurality of pixels,for example, by a pixel grid of 3×3 pixels. In some implementations, thebase caller 144 processes sequencing images that have differentintensity values for pixels in a given pixel grid to characterizerespective intensity profiles of a given cluster at different sequencingcycles. In response to the processing, in one implementation, the basecaller 144 generates outputs that specify respective base calls for thegiven cluster at the different sequencing cycles by referencing only acenter pixel in the given pixel grid that contains a center of the givencluster. That is, the respective base calls are made with respect toonly the center pixel even though the given pixel grid in its entiretycharacterizes the intensity profiles of the given cluster. Thenon-center pixels of the given pixel grid are analyzed by the basecaller 144 to generate the base calls; however, only the center pixel isused to express the base calls.

In implementations in which only the center pixels are used to expressthe base calls, base call-directed state generation can be challengingbecause no base calls are available to determine the states ofnon-cluster pixels. This limitation can be compensated by techniquesdiscussed below.

FIG. 26 illustrates one implementation of generating 2600 stateinformation for non-cluster pixels using previously called bases. Forpixels that contain cluster centers, i.e., the cluster pixels 2602,previous base calls of clusters are used 2612 to generate state data, asdiscussed above, for example, with respect to FIG. 16 to FIG. 20C andFIGS. 24 and 25 .

For pixels that do not contain cluster centers, i.e., the non-clusterpixels 2608, state data of nearest cluster pixels is used 2618 togenerate state data, in accordance with one implementation. In anotherimplementation, an average of state data of all other cluster pixels isused 2628 to generate state data for the non-cluster pixels 2608.

State Input

FIG. 27 illustrates different implementations of generating state data2700 for base calling. The technology disclosed generates stateinformation 2722 for an “entity” 2702 based on its current configurationi and past configurations i−1, i−2, . . . , 1. Examples of the entity2702 include any type of signal measurements, pixel measurements,voltage measurements, current measurements, pH scale measurements,intermediate outputs of processing, convolved features (e.g., featuremaps), compressed features (e.g., compressed feature maps), differenttypes of outputs (e.g., softmax scores, sigmoid scores, regressionscores), base calls, and reads. A current base call is then made independence upon the state information 2722 and the current configurationi of the entity 2702.

Note that to combine the state information 2722 with the entity 2702, insome implementations, the dimensionality of the state information 2722may need to be reconciled with that of the entity 2702 (e.g., makecompatible by matching), or vice-versa. In different implementations,this can be achieved by dimensionality-altering operations like cloning,padding (e.g., zero padding), concatenation, convolution, summing,transpose convolutions, etc. For example, when the state information2722 has a dimensionality of 1×1 and the entity 2702 has adimensionality of 3×3, nine clones of the state information 2722 can beconcatenated with the entity 2702.

Feeding State Input

FIG. 28 illustrates different implementations of feeding 2800 state dataat various aspects of processing of a base calling operation. In oneimplementation, the base caller 144 can have a variety of processingmodules 1-n (e.g., pre-processing layers, neural network layers,post-processing layers, output layers). State data 2802 can be providedto any of the processing modules 1-n of the base caller 144 to generatebase calls 2808. For example, the state data 2802 can be combined withinput data 2802 (e.g., input image(s)) for processing by a firstprocessing module (e.g., a first convolution layer) of the base caller144. In another example, the state data 2802 can be combined with anintermediate output 2812 of the base caller 144 (e.g., intermediatefeature map(s) produced by a preceding convolution layer). In yetanother example (not depicted), the state data 2802 can be combined witha final output of the base caller 144 (e.g., final feature map(s)produced by a final convolution layer). In such an implementation, acombination of the state data 2802 and the final output can be processedby an output layer/base calling layer (e.g., a softmax layer, aregression layer, a sigmoid layer) to generate the base calls 2808.

State-Based and Neural Network-Based Base Caller

The following discussion focuses on a neural network-based base caller2900 described herein. The neural network-based base caller 2900 is oneimplementation of the base caller 144, and collectively referred toherein as “DeepRTA.” First, the input to the neural network-based basecaller 2900 is described, in accordance with one implementation. Then,examples of the structure and form of the neural network-based basecaller 2900 are provided. Finally, the output of the neuralnetwork-based base caller 2900 is described, in accordance with oneimplementation.

A data flow logic provides sequencing images to the neural network-basedbase caller 2900 for base calling. The neural network-based base caller2900 accesses the sequencing images on a patch-by-patch basis (or atile-by-tile basis). Each of the patches is a sub-grid (or sub-array) ofpixelated units in the grid of pixelated units that forms the sequencingimages. The patches have dimensions q×r of the sub-grid of pixelatedunits, where q (width) and r (height) are any numbers ranging from 1 and10000 (e.g., 3×3, 5×5, 7×7, 10×10, 15×15, 25×25, 64×64, 78×78, 115×115).In some implementations, q and r are the same. In other implementations,q and r are different. In some implementations, the patches extractedfrom a sequencing image are of the same size. In other implementations,the patches are of different sizes. In some implementations, the patchescan have overlapping pixelated units (e.g., on the edges).

Sequencing produces m sequencing images per sequencing cycle forcorresponding m image channels. That is, each of the sequencing imageshas one or more image (or intensity) channels (analogous to the red,green, blue (RGB) channels of a color image). In one implementation,each image channel corresponds to one of a plurality of filterwavelength bands. In another implementation, each image channelcorresponds to one of a plurality of imaging events at a sequencingcycle. In yet another implementation, each image channel corresponds toa combination of illumination with a specific laser and imaging througha specific optical filter. The image patches are tiled (or accessed)from each of the m image channels for a particular sequencing cycle. Indifferent implementations such as 4-, 2-, and 1-channel chemistries, mis 4 or 2. In other implementations, m is 1, 3, or greater than 4. Inother implementations, the images can be in blue and violet colorchannels instead of or in addition to the red and green color channels.

Consider, for example, that a sequencing run is implemented using twodifferent image channels: a blue channel and a green channel. Then, ateach sequencing cycle, the sequencing run produces a blue image and agreen image. This way, for a series of k sequencing cycles of thesequencing run, a sequence of k pairs of blue and green images isproduced as output and stored as the sequencing images. Accordingly, asequence of k pairs of blue and green image patches is generated for thepatch-level processing by the neural network-based base caller 2900.

The input image data to the neural network-based base caller 2900 for asingle iteration of base calling (or a single instance of forward passor a single forward traversal) comprises data for a sliding window ofmultiple sequencing cycles. The sliding window can include, for example,a current sequencing cycle, one or more preceding sequencing cycles, andone or more successive sequencing cycles.

In one implementation, the input image data comprises data for threesequencing cycles, such that data for a current (time t) sequencingcycle to be base called is accompanied with (i) data for a leftflanking/context/previous/preceding/prior (time t−1) sequencing cycleand (ii) data for a right flanking/context/next/successive/subsequent(time t+1) sequencing cycle.

In another implementation, the input image data comprises data for fivesequencing cycles, such that data for a current (time t) sequencingcycle to be base called is accompanied with (i) data for a first leftflanking/context/previous/preceding/prior (time t−1) sequencing cycle,(ii) data for a second left flanking/context/previous/preceding/prior(time t−2) sequencing cycle, (iii) data for a first rightflanking/context/next/successive/subsequent (time t+1), and (iv) datafor a second right flanking/context/next/successive/subsequent (timet+2) sequencing cycle.

In yet another implementation, the input image data comprises data forseven sequencing cycles, such that data for a current (time t)sequencing cycle to be base called is accompanied with (i) data for afirst left flanking/context/previous/preceding/prior (time t−1)sequencing cycle, (ii) data for a second leftflanking/context/previous/preceding/prior (time t−2) sequencing cycle,(iii) data for a third left flanking/context/previous/preceding/prior(time t−3) sequencing cycle, (iv) data for a first rightflanking/context/next/successive/subsequent (time t+1), (v) data for asecond right flanking/context/next/successive/subsequent (time t+2)sequencing cycle, and (vi) data for a third rightflanking/context/next/successive/subsequent (time t+3) sequencing cycle.In other implementations, the input image data comprises data for asingle sequencing cycle. In yet other implementations, the input imagedata comprises data for 10, 15, 20, 30, 58, 75, 92, 130, 168, 175, 209,225, 230, 275, 318, 325, 330, 525, or 625 sequencing cycles.

The neural network-based base caller 2900 processes the image patchesthrough its convolution layers and produces an alternativerepresentation, according to one implementation. The alternativerepresentation is then used by an output layer (e.g., a softmax layer)for generating a base call for either just the current (time t)sequencing cycle or each of the sequencing cycles, i.e., the current(time t) sequencing cycle, the first and second preceding (time t−1,time t−2) sequencing cycles, and the first and second succeeding (timet+1, time t+2) sequencing cycles. The resulting base calls form thesequencing reads.

In one implementation, the neural network-based base caller 2900 outputsa base call for a single target cluster for a particular sequencingcycle. In another implementation, the neural network-based base caller2900 outputs a base call for each target cluster in a plurality oftarget clusters for the particular sequencing cycle. In yet anotherimplementation, the neural network-based base caller 2900 outputs a basecall for each target cluster in a plurality of target clusters for eachsequencing cycle in a plurality of sequencing cycles, thereby producinga base call sequence for each target cluster.

In one implementation, the neural network-based base caller 2900 is amultilayer perceptron (MLP). In another implementation, the neuralnetwork-based base caller 2900 is a feedforward neural network. In yetanother implementation, the neural network-based base caller 2900 is afully-connected neural network. In a further implementation, the neuralnetwork-based base caller 2900 is a fully convolution neural network. Inyet further implementation, the neural network-based base caller 2900 isa semantic segmentation neural network. In yet another furtherimplementation, the neural network-based base caller 2900 is agenerative adversarial network (GAN). In yet another implementation, theneural network-based base caller 2900 includes multi-headed attentionmechanisms like Transformers, BERTs, and DETRs.

In one implementation, the neural network-based base caller 2900 is aconvolution neural network (CNN) with a plurality of convolution layers.In another implementation, the neural network-based base caller 2900 isa recurrent neural network (RNN) such as a long short-term memorynetwork (LS™), bi-directional LS™ (Bi-LS™), or a gated recurrent unit(GRU). In yet another implementation, the neural network-based basecaller 2900 includes both a CNN and an RNN.

In yet other implementations, the neural network-based base caller 2900can use 1D convolutions, 2D convolutions, 3D convolutions, 4Dconvolutions, 5D convolutions, dilated or atrous convolutions, transposeconvolutions, depthwise separable convolutions, pointwise convolutions,1×1 convolutions, group convolutions, flattened convolutions, spatialand cross-channel convolutions, shuffled grouped convolutions,Transformers, BERTs, spatial separable convolutions, and deconvolutions.The neural network-based base caller 2900 can use one or more lossfunctions such as logistic regression/log loss, multi-classcross-entropy/softmax loss, binary cross-entropy loss, mean-squarederror loss, L1 loss, L2 loss, smooth L1 loss, and Huber loss. The neuralnetwork-based base caller 2900 can use any parallelism, efficiency, andcompression schemes such TFRecords, compressed encoding (e.g., PNG),sharding, parallel calls for map transformation, batching, prefetching,model parallelism, data parallelism, and synchronous/asynchronousstochastic gradient descent (SGD). The neural network-based base caller2900 can include upsampling layers, downsampling layers, recurrentconnections, gates and gated memory units (like an LS™ or GRU), residualblocks, residual connections, highway connections, skip connections,peephole connections, activation functions (e.g., non-lineartransformation functions like rectifying linear unit (ReLU), leaky ReLU,exponential liner unit (ELU), sigmoid and hyperbolic tangent (tanh)),batch normalization layers, regularization layers, dropout, poolinglayers (e.g., max or average pooling), global average pooling layers,and attention mechanisms.

The neural network-based base caller 2900 is trained usingbackpropagation-based gradient update techniques. Example gradientdescent techniques that can be used for training the neuralnetwork-based base caller 2900 include stochastic gradient descent,batch gradient descent, and mini-batch gradient descent. Some examplesof gradient descent optimization algorithms that can be used to trainthe neural network-based base caller 2900 are Momentum, Nesterovaccelerated gradient, Adagrad, Adadelta, RMSprop, Adam, AdaMax, Nadam,and AMSGrad.

In one implementation, the neural network-based base caller 2900 uses aspecialized architecture to segregate processing of data for differentsequencing cycles. The motivation for using the specialized architectureis described first. As discussed above, the neural network-based basecaller 2900 processes image patches for a current sequencing cycle, oneor more preceding sequencing cycles, and one or more successivesequencing cycles. Data for additional sequencing cycles providessequence-specific context. The neural network-based base caller 2900learns the sequence-specific context during training and base callsthem. Furthermore, data for pre- and post-sequencing cycles providessecond order contribution of pre-phasing and phasing signals to thecurrent sequencing cycle.

However, images captured at different sequencing cycles and in differentimage channels are misaligned and have residual registration error withrespect to each other. To account for this misalignment, the specializedarchitecture comprises spatial convolution layers that do not mixinformation between sequencing cycles and only mix information within asequencing cycle.

Spatial convolution layers (or spatial logic) use so-called “segregatedconvolutions” that operationalize the segregation by independentlyprocessing data for each of a plurality of sequencing cycles through a“dedicated, non-shared” sequence of convolutions. The segregatedconvolutions convolve over data and resulting feature maps of only agiven sequencing cycle, i.e., intra-cycle, without convolving over dataand resulting feature maps of any other sequencing cycle.

Consider, for example, that the input image data comprises (i) currentimage patch for a current (time t) sequencing cycle to be base called,(ii) previous image patch for a previous (time t−1) sequencing cycle,and (iii) next image patch for a next (time t+1) sequencing cycle. Thespecialized architecture then initiates three separate convolutionpipelines, namely, a current convolution pipeline, a previousconvolution pipeline, and a next convolution pipeline. The current dataprocessing pipeline receives as input the current image patch for thecurrent (time t) sequencing cycle and independently processes it througha plurality of spatial convolution layers to produce a so-called“current spatially convolved representation” as the output of a finalspatial convolution layer. The previous convolution pipeline receives asinput the previous image patch for the previous (time t−1) sequencingcycle and independently processes it through the plurality of spatialconvolution layers to produce a so-called “previous spatially convolvedrepresentation” as the output of the final spatial convolution layer.The next convolution pipeline receives as input the next image patch forthe next (time t+1) sequencing cycle and independently processes itthrough the plurality of spatial convolution layers to produce aso-called “next spatially convolved representation” as the output of thefinal spatial convolution layer.

In some implementations, the current, previous, and next convolutionpipelines are executed in parallel. In some implementations, the spatialconvolution layers are part of a spatial convolution network (orsubnetwork) within the specialized architecture.

The neural network-based base caller 2900 further comprises temporalconvolution layers (or temporal logic) that mix information betweensequencing cycles, i.e., inter-cycles. The temporal convolution layersreceive their inputs from the spatial convolution network and operate onthe spatially convolved representations produced by the final spatialconvolution layer for the respective data processing pipelines.

The inter-cycle operability freedom of the temporal convolution layersemanates from the fact that the misalignment property, which exists inthe image data fed as input to the spatial convolution network, ispurged out from the spatially convolved representations by the stack, orcascade, of segregated convolutions performed by the sequence of spatialconvolution layers.

Temporal convolution layers use so-called “combinatory convolutions”that groupwise convolve over input channels in successive inputs on asliding window basis. In one implementation, the successive inputs aresuccessive outputs produced by a previous spatial convolution layer or aprevious temporal convolution layer.

In some implementations, the temporal convolution layers are part of atemporal convolution network (or subnetwork) within the specializedarchitecture. The temporal convolution network receives its inputs fromthe spatial convolution network. In one implementation, a first temporalconvolution layer of the temporal convolution network groupwise combinesthe spatially convolved representations between the sequencing cycles.In another implementation, subsequent temporal convolution layers of thetemporal convolution network combine successive outputs of previoustemporal convolution layers. The output of the final temporalconvolution layer is fed to an output layer that produces an output. Theoutput is used to base call one or more clusters at one or moresequencing cycles.

Additional details about the neural network-based base caller 2900 canbe found in U.S. Provisional Patent Application No. 62/821,766, titled“ARTIFICIAL INTELLIGENCE-BASED SEQUENCING,” (Attorney Docket No. ILLM1008-9/IP-1752-PRV), filed on Mar. 21, 2019, which is incorporatedherein by reference.

Per-Pixel State Input

FIG. 29 illustrates one implementation of state-based and neuralnetwork-based base calling. In the example illustrated in FIG. 29 , afirst window of sequencing cycles includes sequencing cycles N−2, N−1,N, N+1, and N+2. In one implementation, respective image patches(sequencing images/cluster images) 2914, 2924, 2934, 2944, and 2954 forthe respective sequencing cycles N−2, N−1, N, N+1, and N+2 arepixel-wise combined (e.g., concatenated) with state data 2912 for thecurrent sequencing cycle N. State data 2912 can be determined using anyof the techniques discussed above.

The image patches 2914, 2924, 2934, 2944, and 2954 are collectivelyreferred to as cluster images (sequencing images) 2950. The per-pixel,per-channel state data 2940 includes five instances of the state data2912 because there are five sequencing cycles N−2, N−1, N, N+1, and N+2.

State data 2912 has per-pixel state values for corresponding pixels inthe cluster images 2950, similar to the implementation discussed abovewith respect to FIGS. 3 and 4 . Also, in one implementation, because thecluster images 2950 have two channels (intensity channels), the statedata 2912 also has two state channels. In other implementations, thestate data 2912 can have only one channel or have more than twochannels, as discussed above in different implementations.

At a high-level, per-pixel, per-channel state data 2940 is combined withthe cluster images 2950 for processing by a spatial logic 106 (orspatial network or spatial subnetwork or spatial convolution neuralnetwork). The combination of the per-pixel, per-channel state data 2940and the cluster images 2950 is separately processed through a spatiallogic 106 to generate respective spatial maps 2916, 2926, 2936, 2946,and 2956 (or intermediate results or spatial output sets or spatialfeature map sets) for the respective sequencing cycles N−2, N−1, N, N+1,and N+2. The spatial convolution network 106 can use 1D, 2D, or 3Dconvolutions.

The spatial logic 106 includes a sequence (or cascade) of spatialconvolution layers. Each spatial convolution layer has a filter bankwith a plurality of spatial convolution filters that implementsegregated convolutions. Accordingly, each spatial convolution layerproduces as output a plurality of spatial feature maps. The number ofspatial feature maps produced by a subject spatial convolution layer isa function of the number of spatial convolution filters configured inthe subject spatial convolution layer. For example, if the subjectspatial convolution layer has fourteen spatial convolution filters, thenthe subject spatial convolution layer produces fourteen spatial featuremaps. From an aggregate perspective, the fourteen spatial feature mapscan be considered a spatial feature map volume (or tensor) with fourteenchannels (or depth dimension=fourteen).

Furthermore, a next spatial convolution layer that follows the subjectspatial convolution layer can also be configured with fourteen spatialconvolution filters. In such a case, the next spatial convolution layerprocesses, as input, the fourteen spatial feature maps generated thesubject spatial convolution layer, and itself generates fourteen newspatial feature maps as output. FIG. 29 shows the five spatial featuremap sets 2916, 2926, 2936, 2946, and 2956 generated by a final spatialconvolution layer of the spatial network 106 for the respectivesequencing cycles N−2, N−1, N, N+1, and N+2. For example, each of thefive spatial feature map sets 2916, 2926, 2936, 2946, and 2956 havefourteen feature maps.

In another example, a sequence of seven spatial feature map sets can begenerated by a cascade of seven spatial convolution layers of thespatial network 106. A combination of per-cycle input patch data andstate data for a subject sequencing cycle i can have, for example, aspatial dimensionality of 115×115 and a depth dimensionality of two (dueto the two image channels in the original sequencing images). In oneimplementation, each of the seven spatial convolution layers uses 3×3convolutions that reduce the spatial dimensionality of successivespatial feature map volumes by two, for example, from 10×10 to 8×8.

The first spatial feature map volume can have spatial dimensions 113×113(i.e., reduced from 115×115 by the 3×3 convolutions of the first spatialconvolution layer) and a depth dimension of 14 (i.e., fourteen featuremaps or fourteen channels due to fourteen spatial convolution filters inthe first spatial convolution layer). The second spatial feature mapvolume can have spatial dimensions 111×111 (i.e., reduced from 113×113by the 3×3 convolutions of the second spatial convolution layer) and adepth dimension of 14 (i.e., fourteen feature maps or fourteen channelsdue to fourteen spatial convolution filters in the second spatialconvolution layer). The third spatial feature map volume can havespatial dimensions 109×109 (i.e., reduced from 111×111 by the 3×3convolutions of the third spatial convolution layer) and a depthdimension of 14 (i.e., fourteen feature maps or fourteen channels due tofourteen spatial convolution filters in the third spatial convolutionlayer). The fourth spatial feature map volume can have spatialdimensions 107×107 (i.e., reduced from 109×109 by the 3×3 convolutionsof the fourth spatial convolution layer) and a depth dimension of 14(i.e., fourteen feature maps or fourteen channels due to fourteenspatial convolution filters in the fourth spatial convolution layer).The fifth spatial feature map volume can have spatial dimensions 105×105(i.e., reduced from 107×107 by the 3×3 convolutions of the fifth spatialconvolution layer) and a depth dimension of 14 (i.e., fourteen featuremaps or fourteen channels due to fourteen spatial convolution filters inthe fifth spatial convolution layer). The sixth spatial feature mapvolume can have spatial dimensions 103×103 (i.e., reduced from 105×105by the 3×3 convolutions of the sixth spatial convolution layer) and adepth dimension of 14 (i.e., fourteen feature maps or fourteen channelsdue to fourteen spatial convolution filters in the sixth spatialconvolution layer). The seventh spatial feature map volume can havespatial dimensions 101×101 (i.e., reduced from 103×103 by the 3×3convolutions of the seventh spatial convolution layer) and a depthdimension of 14 (i.e., fourteen feature maps or fourteen channels due tofourteen spatial convolution filters in the seventh spatial convolutionlayer).

Analogizing to the multi-cycle example illustrated in FIG. 29 , for thefive sequencing cycles N−2, N−1, N, N+1, and N+2 and the five per-cycleimage patches 2914, 2924, 2934, 2944, and 2954 and the five instances ofthe state data 2912, the spatial logic 106 separately produces fiverespective sequences of the seven spatial feature map volumes, with thespatial maps 2916, 2926, 2936, 2946, and 2956 in FIG. 29 beingequivalent to five separate instances of a final spatial feature mapvolume.

The spatial maps 2916, 2926, 2936, 2946, and 2956 are processed by atemporal logic 2928 to generate base calls 2930 for the currentsequencing cycle N.

Per-Cluster/Per-Well States

FIG. 30 illustrates one implementation of using per-cluster state datafor base calling clusters. Different than FIG. 29 , in FIG. 30 , thestate data is not combined with the input cluster images 2914, 2924,2934, 2944, and 2954. Instead, the state data is combined with theoutput of the spatial logic 106. Note that the cluster images 2914,2924, 2934, 2944, and 2954 contain both cluster pixels and non-clusterpixels. As a result, the spatial maps 3016, 3026, 3036, 3046, and 3056contain spatially convolved features emanating from both cluster pixelsand non-cluster pixels.

In some implementations, a cluster feature filtering logic 3030 usescluster center locations 3002 to filter out those features from thespatial maps 3016, 3026, 3036, 3046, and 3056 that correspond tonon-cluster pixels. The resulting filtered, per-cluster spatial maps3054, 3064, 3074, 3084, and 3094 contain only those features thatcorrespond to cluster pixels, collectively referred to as clusterfeatures 3004. In one implementation, the dimensionality of the spatialmaps 3016, 3026, 3036, 3046, and 3056 is 101×101, and the dimensionalityof the filtered, per-cluster spatial maps 3054, 3064, 3074, 3084, and3094 is 25×25. In such an implementation, the 25×25 x k tensor includesspatial features that are produced as a result of applying successiveconvolution operations on data corresponding to cluster pixels,including the original cluster pixels and successively generatedconvolved features of the cluster pixels.

Unlike FIG. 29 in which the state data 2940 is combined with the clusterimages 2950 at the starting input to the neural network-based basecaller 2900, in FIG. 30 , per-cluster states 3042 for the currentsequencing cycle N are combined with intermediate outputs of the neuralnetwork-based base caller 2900, i.e., the filtered, per-cluster spatialmaps 3054, 3064, 3074, 3084, and 3094.

In some implementations, the dimensionality of the per-cluster states3042 is modified to conform with the dimensionality of the filtered,per-cluster spatial maps 3054, 3064, 3074, 3084, and 3094 (e.g., bytrimming dimensions, adding dimensions, padding (e.g., zero padding),cloning, etc.). In different implementations, the respective fiveinstances of the per-cluster states 3042 and the filtered, per-clusterspatial maps 3054, 3064, 3074, 3084, and 3094 can be respectivelycombined using techniques discussed above, such as concatenation,summation, element-wise multiplication, element-wise multiplication andsummation (convolution), and so on.

Then, according to the illustrated example in FIG. 30 , the respectivefive combinations of the respective filtered, per-cluster spatial maps3054, 3064, 3074, 3084, and 3094 and the respective five instances ofthe per-cluster states 3042 are processed by the temporal logic 2928 togenerate base calls 3060 for the current sequencing cycle N. The basecalls 3060 are made for clusters corresponding to the cluster pixels andtherefore the cluster features 3004.

The discussion now turns to different variations of the per-clusterstates 3042 and how these variations are determined according todifferent implementations of the technology disclosed.

FIG. 31 illustrates one implementation of generating 3100 per-clusterstates using historic intensity values of corresponding cluster pixels.In FIG. 31 , the per-cluster states 3042 are determined by accumulatingsummary statistics of historic intensity values of the cluster pixelsfrom the previous sequencing cycles and the current sequencing cycle, asdiscussed above, for example, using minimum value selection function,maximum value selection function, averaging function, exponentialweighted averaging function, and so on. Then, the per-cluster states3042 are combined with spatially convolved features of the clusterpixels and of a current base calling iteration/operation to generatebase calls for the corresponding clusters. These steps are illustratedby actions 3102, 3112, 3122, and 3132 of FIG. 31 .

FIG. 32 illustrates one implementation of generating 3200 per-clusterstates using historic feature values of spatially convolved featurescorresponding to cluster pixels. In FIG. 31 , the per-cluster states3042 are determined by accumulating summary statistics of historicfeature values of the spatially convolved features corresponding to thecluster pixels from the previous sequencing cycles and the currentsequencing cycle, as discussed above, for example, using minimum valueselection function, maximum value selection function, averagingfunction, exponential weighted averaging function, and so on. Then, theper-cluster states 3042 are combined with spatially convolved featuresof the cluster pixels and of the current base callingiteration/operation to generate base calls for the correspondingclusters. These steps are illustrated by actions 3202, 3212, 3222, and3232 of FIG. 32 .

FIG. 33 illustrates one implementation of generating 3300 clusterintensities by interpolating pixel intensities, and using theinterpolated cluster intensities to generate per-cluster states for basecalling. In one implementation, intensity values of a group of pixels inthe sequencing images can be interpolated (e.g., by using linear,bilinear, cubic interpolation) to estimate cluster intensities. In someimplementations, cluster center locations and cluster shape data can beused to select a given group of pixels to interpolate clusterintensities for a corresponding cluster. In other implementations,cluster shape data can be used to use weighted intensities of partialpixels to determine the cluster intensities.

Summary statistics for cluster intensities interpolated at the previoussequencing cycles and the current sequencing cycle can be accumulated togenerate the per-cluster states 3042 for the current sequencing cycle,as discussed above, for example, using minimum value selection function,maximum value selection function, averaging function, exponentialweighted averaging function, and so on. Then, the per-cluster states3042 are combined with spatially convolved features of the clusterpixels and of the current base calling iteration/operation to generatebase calls for the corresponding clusters. These steps are illustratedby actions 3302, 3312, 3322, and 3332 of FIG. 32 .

RTA Coefficients as Per-Cluster States

FIG. 34 illustrates one implementation of generating 3400 per-clusterstates using a Real Time Analysis (RTA) base caller separate from theneural network-based base caller 2900.

Regarding the RTA base caller, The RTA is a base caller that can uselinear intensity extractors to extract features from sequencing imagesfor base calling. The following discussion describes one implementationof intensity extraction and base calling by RTA. In this implementation,RTA can perform a template generation step to produce a template imagethat identifies locations of clusters on a tile using sequencing imagesfrom some number of initial sequencing cycles called template cycles.The template image can be used as a reference for subsequentregistration and intensity extraction steps. The template image can begenerated by detecting and merging bright spots in each sequencing imageof the template cycles, which in turn involves sharpening a sequencingimage (e.g., using the Laplacian convolution), determining an “ON”threshold by a spatially segregated Otsu approach, and subsequentfive-pixel local maximum detection with subpixel location interpolation.In another example, locations of clusters on a tile can be identifiedusing fiducial markers. A solid support upon which a biological specimenis imaged can include such fiducial markers, to facilitate determinationof the orientation of the specimen or the image thereof in relation toprobes that are attached to the solid support. Exemplary fiducials caninclude but are not limited to beads (with or without fluorescentmoieties or moieties such as nucleic acids to which labeled probes canbe bound), fluorescent molecules attached at known or determinablefeatures, or structures that combine morphological shapes withfluorescent moieties. Exemplary fiducials are set forth in U.S. PatentPublication No. 2002/0150909, which is incorporated herein by reference.

RTA can then register a current sequencing image against the templateimage. This can be achieved by using image correlation to align thecurrent sequencing image to the template image on a sub-region, or byusing non-linear transformations (e.g., a full six-parameter linearaffine transformation).

RTA can generate a color matrix to correct cross-talk between colorchannels of the sequencing images. RTA can implement empirical phasingcorrection to compensate noise in the sequencing images caused by phaseerrors.

After different corrections are applied to the sequencing images, RTAcan extract signal intensities for each spot location in the sequencingimages. For example, for a given spot location, signal intensity may beextracted by determining a weighted average of the intensity of thepixels in a spot location. For example, a weighted average of the centerpixel and neighboring pixels may be performed using bilinear or bicubicinterpolation. In some implementations, each spot location in the imagemay comprise a few pixels (e.g., 1-5 pixels).

RTA can then spatially normalize the extracted signal intensities toaccount for variation in illumination across the sampled imaged. Forexample, intensity values may be normalized such that a 5th and 95thpercentiles have values of 0 and 1, respectively. The normalized signalintensities for the image (e.g., normalized intensities for eachchannel) may be used to calculate mean chastity for the plurality ofspots in the image.

In some implementations, RTA can use an equalizer to maximize thesignal-to-noise ratio of the extracted signal intensities. The equalizercan be trained (e.g., using least square estimation, adaptiveequalization algorithm) to maximize the signal-to-noise ratio of clusterintensity data in sequencing images. In one implementation, theequalizer can include trained coefficients to correct for spatialcrosstalk and other forms of crosstalk and noises. In someimplementations, the equalizer can be a single lookup table (LUT). Inother implementations, the equalizer can be a LUT bank with a pluralityof LUTs with subpixel resolution, also referred to as “equalizerfilters” or “convolution kernels.” In one implementation, the number ofLUTs in the equalizer can depend on the number of subpixels into whichpixels of the sequencing images can be divided. For example, if thepixels are divisible into n by n subpixels (e.g., 5×5 subpixels), thenthe equalizer can generate n2 LUTs (e.g., 25 LUTs). In yetimplementations, the equalizer can be based on Fast Fourier Transforms(FFTs). In yet implementations, the equalizer can be based on Winogradconvolutions.

In one implementation of training the equalizer, data from thesequencing images is binned by well subpixel location. For example, fora 5×5 LUT, 1/25th of the wells have a center that is in bin (1,1) (e.g.,the upper left corner of a sensor pixel), 1/25th of the wells are in bin(1,2), and so on. In one implementation, the equalizer coefficients foreach bin are determined using least squares estimation on the subset ofdata from the wells corresponding to the respective bins. This way theresulting estimated equalizer coefficients are different for each bin.

Each LUT/equalizer filter/convolution kernel has a plurality ofcoefficients that are learned from the training. In one implementation,the number of coefficients in a LUT corresponds to the number of pixelsthat are used for base calling a cluster. For example, if a local gridof pixels (image or pixel patch) that is used to base call a cluster isof size p×p (e.g., 9×9 pixel patch), then each LUT has p2 coefficients(e.g., 81 coefficients).

In one implementation, the training produces equalizer coefficients thatare configured to mix/combine intensity values of pixels that depictintensity emissions from a target cluster being base called andintensity emissions from one or more adjacent clusters in a manner thatmaximizes the signal-to-noise ratio. The signal maximized in thesignal-to-noise ratio is the intensity emissions from the targetcluster, and the noise minimized in the signal-to-noise ratio is theintensity emissions from the adjacent clusters, i.e., spatial crosstalk,plus some random noise (e.g., to account for background intensityemissions). The equalizer coefficients are used as weights and themixing/combining includes executing element-wise multiplication betweenthe equalizer coefficients and the intensity values of the pixels tocalculate a weighted sum of the intensity values of the pixels, i.e., aconvolution operation.

RTA can perform base calling by fitting a mathematical model to theoptimized intensity data. Suitable mathematical models that can be usedinclude, for example, a k-means clustering algorithm, a k-means-likeclustering algorithm, expectation maximization clustering algorithm, ahistogram based method, and the like. Four Gaussian distributions may befit to the set of two-channel intensity data such that one distributionis applied for each of the four nucleotides represented in the data set.In one particular implementation, an expectation maximization (EM)algorithm may be applied. As a result of the EM algorithm, for each X, Yvalue (referring to each of the two channel intensities respectively) avalue can be generated which represents the likelihood that a certain X,Y intensity value belongs to one of four Gaussian distributions to whichthe data is fitted. Where four bases give four separate distributions,each X, Y intensity value will also have four associated likelihoodvalues, one for each of the four bases. The maximum of the fourlikelihood values indicates the base call. For example, if a cluster is“OFF” in both channels, the base call is G. If the cluster is “OFF” inone channel and “ON” in another channel the base call is either C or T(depending on which channel is ON), and if the cluster is “ON” in bothchannels the base call is A.

Additional details about RTA can be found in U.S. Nonprovisional patentapplication Ser. No. 15/909,437, titled “OPTICAL DISTORTION CORRECTIONFOR IMAGED SAMPLES,” filed on Mar. 1, 2018; U.S. Nonprovisional patentapplication Ser. No. 14/530,299, titled “IMAGE ANALYSIS USEFUL FORPATTERNED OBJECTS,” filed on Oct. 31, 2014; U.S. Nonprovisional patentapplication Ser. No. 15/153,953, titled “METHODS AND SYSTEMS FORANALYZING IMAGE DATA,” filed on Dec. 3, 2014; U.S. Nonprovisional patentapplication Ser. No. 13/006,206, titled “DATA PROCESSING SYSTEM ANDMETHODS,” filed on Jan. 13, 2011; and U.S. Nonprovisional patentapplication Ser. No. 17/308,035, titled “EQUALIZATION-BASED IMAGEPROCESSING AND SPATIAL CROSSTALK ATTENUATOR,” filed May 4, 2021(Attorney Docket No. ILLM 1032-2/IP-1991-US), all which are incorporatedby reference as if fully set forth herein.

The inter-cluster intensity profile variation among intensity profilesof a large number (e.g., thousands, millions, billions, etc.) ofclusters in the cluster population causes a drop in base callingthroughput and an increase in base calling error rate. To correct theinter-cluster intensity profile variation, the RTA generates per-clustervariation correction coefficients on a cluster-by-cluster basis.

In the two-channel implementation, the per-cluster variation correctioncoefficients comprise an amplification coefficient that accounts forscale variation in the inter-cluster intensity profile variation, andtwo channel-specific offset coefficients that account for shiftvariation along the first and the second intensity channels in theinter-cluster intensity profile variation, respectively. In anotherimplementation, the shift variation is accounted for by using a commonoffset coefficient for the different intensity channels (e.g., the firstand the second intensity channels).

It would be apparent to one skilled in the art that the variationcorrection logic of the RTA can be analogously applied to sequencingimages generated using one-channel implementation, four-channelimplementation, and so on. For example, in the case of four-channelimplementation, four channel-specific offset coefficients are determinedto correct shift variations in four intensity channels, respectively.

The per-cluster variation correction coefficients for a target clusterare generated at a current sequencing cycle of the sequencing run basedon combining analysis of historic intensity statistics determined forthe target cluster at preceding sequencing cycles of the sequencing runwith analysis of current intensity statistics determined for the targetcluster at the current sequencing cycle. The per-cluster variationcorrection coefficients are used to correct next intensity readingsregistered for the target cluster a next sequencing cycle of thesequencing run. The corrected next intensity readings are used to basecall the target cluster at the next sequencing cycle. The result ofrepeatedly applying respective per-cluster variation correctioncoefficients to respective intensity profiles of respective clusters atsuccessive sequencing cycles of the sequencing run is that the intensityprofiles become coincidental and anchored to the origin (e.g., at thebottom lower corner of the trapezoids).

Current Sequencing Cycle

At the current sequencing cycle i, the sequencer generates sequencingimages. The sequencing images contain current intensity data registeredfor the target cluster at the current sequencing cycle i, along withcontaining current intensity data registered for multiple clusters inthe cluster population.

The current intensity data is provided to the RTA. The RTA processes thecurrent intensity data and generates a current base call for the targetcluster at the current sequencing cycle i.

At the current sequencing cycle i, the intensity profile of the targetcluster includes the current intensity data, and current historicintensity data registered for the target cluster at those sequencingcycles of the sequencing run that precede the current sequencing cyclei, i.e., preceding sequencing cycles 1 to i−1. We collectively refer tothe current intensity data and the current historic intensity data ascurrent available intensity data.

In the intensity profile, the four intensity distributions correspond tothe four bases A, C, T, and G. In one implementation, the current basecall is made by determining which of the four intensity distributionsthe current intensity data belongs to. In some implementations, this isaccomplished by using an expectation maximization algorithm. Theexpectation maximization algorithm iteratively maximizes the likelihoodof observing means (centroids) and distributions (covariances) that bestfit the current available intensity data.

Once the four intensity distributions are determined at the currentsequencing cycle i by using the expectation maximization algorithm, thelikelihoods of the current intensity data belonging to each of the fourintensity distributions are calculated. The greatest likelihood givesthe current base call. As an example, consider that “m, n” are theintensity values of the current intensity data in the first and secondintensity channels, respectively. The expectation maximization algorithmgenerates four values that represent the likelihoods of the “m, n”intensity values belonging to each of the four intensity distributions.The maximum of the four values identifies the called base.

In other implementations, a k-means clustering algorithm, a k-means-likeclustering algorithm, a histogram based method, and the like can be usedfor base calling.

Next Sequencing Cycle

At the next sequencing cycle i+1, an intensity correction parametersdeterminer determines intensity correction parameters for the targetcluster based on the current base call. In the two-channelimplementation, the intensity correction parameters include distributionintensity in the first intensity channel, distribution intensity in thesecond intensity channel, intensity error in the first intensitychannel, intensity error in the second intensity channel, distributioncentroid-to-origin distance, and distribution intensity-to-intensityerror similarity measure.

We define each of the intensity correction parameters as follows:

-   -   1) A distribution intensity in the first intensity channel is        the intensity value in the first intensity channel at a centroid        of a base-specific intensity distribution to which the target        cluster belongs at the current sequencing cycle i. Note that the        base-specific intensity distribution is the basis for calling        the current base call.    -   2) A distribution intensity in the second intensity channel is        the intensity value in the second intensity channel at the        centroid of the base-specific intensity distribution.    -   3) An intensity error in the first intensity channel is the        difference between the measured intensity value of the current        intensity data in the first intensity channel and the        distribution intensity in the first intensity channel.    -   4) An intensity error in the second intensity channel is the        difference between the measured intensity value of the current        intensity data in the second intensity channel and the        distribution intensity in the second intensity channel.    -   5) A distribution centroid-to-origin distance is the Euclidean        distance between the centroid of the base-specific intensity        distribution and the origin of the multi-dimensional space in        which the base-specific intensity distribution was fitted (e.g.,        by using the expectation maximization algorithm). In other        implementations, distance metrics such as the Mahalanobis        distance and the minimum covariance determinant (MCD) distances,        and their associated centroid estimators can be used.    -   6) A distribution intensity-to-intensity error similarity        measure is the summation of channel-wise dot products between        the distribution intensities and the intensity errors in the        first and second intensity channels.

An accumulated intensity correction parameter determiner accumulates theintensity correction parameters with historic accumulated intensitycorrection parameters from preceding sequencing cycle i−1 to determineaccumulated intensity correction parameters. Examples of accumulationinclude summing and averaging.

A variation correction coefficients determiner determines variationcorrection coefficients based on the determine accumulated intensitycorrection parameters.

At the next sequencing cycle i+1, the sequencer generates sequencingimages. The sequencing images contain next intensity data registered forthe target cluster at the next sequencing cycle i+1, along withcontaining next intensity data registered for multiple clusters in thecluster population.

An intensity corrector applies the variation correction coefficients tothe next intensity data to generate corrected next intensity data.

At the next sequencing cycle i+1, the intensity profile of the targetcluster includes the corrected next intensity data, and next historicintensity data registered for the target cluster at those sequencingcycles of the sequencing run that precede the next sequencing cycle i+1,i.e., preceding sequencing cycles 1 to i. We collectively refer to thecorrected next intensity data and the next historic intensity data asnext available intensity data.

The corrected next intensity data is provided to the RTA. The RTA canprocess the corrected next intensity data and can generate a next basecall for the target cluster at the next sequencing cycle i+1. Togenerate the next base call, the expectation maximization algorithm canobserve the means (centroids) and the distributions (covariances) basedon the corrected next intensity data to best fit the next availableintensity data.

Once the four intensity distributions are determined at the nextsequencing cycle i+1 by using the expectation maximization algorithm,the likelihoods of the corrected next intensity data belonging to eachof the four intensity distributions are calculated. The greatestlikelihood gives the next base call.

Note that the base calling pipeline is executed on a cluster-by-clusterbasis and is executed in parallel for the multiple clusters in thecluster population. Also, the base calling pipeline is executedrepeatedly for successive sequencing cycles of the sequencing run (e.g.,for successive 150 sequencing cycles of read 1 and another successive150 sequencing cycles of read 2 in a paired-end sequencing run).

Least-Squares Solution

A least-squares solution determines closed-form expressions for theaccumulated intensity correction parameters and the variation correctioncoefficients. The least-squares solution determiner comprises anintensity modeler and a minimizer.

The intensity modeler models the relationship between the measuredintensity for the target cluster and the variation correctioncoefficients according to the following expression:

y _(C,i) =ax _(C,i) +d _(i) +n _(C,i)  Equation (1)

where

-   -   a is the amplification coefficient for the target cluster    -   d_(i) is the channel-specific offset coefficient for intensity        channel i    -   x_(C,i) is the distribution intensity in the intensity channel i        for the target cluster at the current sequencing cycle C    -   Y_(C,i) is the measured intensity in the intensity channel i for        the target cluster at the current sequencing cycle C    -   n_(C,i) is the additive noise in the intensity channel i for the        target cluster at the current sequencing cycle C

The minimizer uses the least-squares solution to minimize the followingexpression:

$\begin{matrix}{{{errorf}\left( {\hat{a},{\hat{d}}_{i}} \right)} = {\sum\limits_{c = 1}^{C}{\sum\limits_{i = 1}^{2}\left( {{\hat{a}x_{C,i}} + {\hat{d}}_{i} - y_{C,i}} \right)^{2}}}} & {{Equation}(2)}\end{matrix}$

where:

-   -   error f is the error function    -   â is the amplification coefficient for the target cluster    -   {circumflex over (d)}_(i) is the channel-specific offset        coefficient for the intensity channel i    -   C is the current sequencing cycle

Using the chain rule, the minimizer calculates two partial derivativesof the error function with respect to the amplification coefficient âand the channel-specific offset coefficients {circumflex over (d)}_(i).The partial derivatives set Equation 2 to zero to minimize the errorfunction:

$\begin{matrix}{\frac{\partial{errorf}}{\partial\hat{a}} = {{\frac{\partial}{\partial\hat{a}}\left\lbrack {\sum\limits_{c = 1}^{C}{\sum\limits_{i = 1}^{2}\left( {{\hat{a}x_{C,i}} + {\hat{d}}_{i} - y_{C,i}} \right)^{2}}} \right\rbrack} = 0}} & {{Equation}(3)}\end{matrix}$ $\begin{matrix}{\frac{\partial{errorf}}{\partial\hat{d_{i}}} = {{\frac{\partial}{\partial\hat{d_{i}}}\left\lbrack {\sum\limits_{c = 1}^{C}{\sum\limits_{i = 1}^{2}\left( {{\hat{a}x_{C,i}} + {\hat{d}}_{i} - y_{C,i}} \right)^{2}}} \right\rbrack} = 0}} & {{Equation}(4)}\end{matrix}$

Channel-specific intensity error e_(c,i) is defined as follows:

e _(C,i) =y _(C,i) −x _(C,i)  Equation (5)

Closed-Form Expressions

The first partial derivative determines a closed-form expression for theamplification coefficient â as follows:

$\begin{matrix}{\frac{\partial{errorf}}{\partial\hat{a}} = {{\sum\limits_{c = 1}^{C}{\sum\limits_{i = 1}^{2}{2{x_{C,i}\left( {{\hat{a}x_{C,i}} + {\hat{d}}_{i} - y_{C,i}} \right)}}}} = 0}} & {{Equation}(6)}\end{matrix}$ $\begin{matrix}{= {{\sum\limits_{c = 1}^{C}{\sum\limits_{i = 1}^{2}{2{x_{C,i}\left( {{\hat{a}x_{C,i}} - x_{C,i} + {\hat{d}}_{i} - y_{C,i} + x_{C,i}} \right)}}}} = 0}} & {{Equation}(7)}\end{matrix}$ $\begin{matrix}{\left. {= {{\sum\limits_{c = 1}^{C}{\sum\limits_{i = 1}^{2}{x_{C,i}^{2}\left( {\hat{a} - 1} \right)}}} + {{\hat{d}}_{i}x_{C,i}} - {e_{C,i}x_{C,i}}}} \right) = 0} & {{Equation}(8)}\end{matrix}$ $\begin{matrix}{= {{\left( {a - 1} \right){\sum\limits_{c = 1}^{C}\left( {x_{C,i}^{2} + x_{C,2}^{2}} \right)}} + {d_{i}{\sum\limits_{c = 1}^{C}x_{C,i}}} + {d_{2}{\sum\limits_{c = 1}^{C}x_{C,2}}} -}} & {{Equation}(9)}\end{matrix}$${{\sum\limits_{c = 1}^{C}{e_{C,1}x_{C,1}}} - {\sum\limits_{c = 1}^{C}{e_{C,2}x_{C,2}}}} = 0$

Closed-form expressions x ₁, x ₂, ē₁, ē₂, xx, and xe for the accumulatedintensity correction parameters recharacterize Equation 9 as follows:

=C(â−1) xx+Cx ₁ {circumflex over (d)} ₁ +Cx ₂ {circumflex over (d)} ₂−Cxe=0  Equation (10)

=(â−1) xx+x ₁ {circumflex over (d)} ₁ +x ₂ {circumflex over (d)} ₂−xe=0  Equation (11)

where:

$\begin{matrix}{{\overset{¯}{x}}_{1} = {\sum\limits_{c = 1}^{C}x_{C,1}}} & {{Intermediate}{Term}(1)}\end{matrix}$ $\begin{matrix}{{\overset{¯}{x}}_{2} = {\sum\limits_{c = 1}^{C}x_{C,2}}} & {{Intermediate}{Term}(2)}\end{matrix}$ $\begin{matrix}{{\overset{¯}{e}}_{1} = {\sum\limits_{c = 1}^{C}e_{c,1}}} & {{Intermediate}{Term}(3)}\end{matrix}$ $\begin{matrix}{{\overset{¯}{e}}_{2} = {\sum\limits_{c = 1}^{C}e_{c,2}}} & {{Intermediate}{Term}(4)}\end{matrix}$ $\begin{matrix}{\overset{\_}{xx} = {\sum\limits_{c = 1}^{C}\left( {x_{C,1}^{2} + x_{C,2}^{2}} \right)}} & {{Intermediate}{Term}(5)}\end{matrix}$ $\begin{matrix}{\overset{\_}{xe} = {\sum\limits_{c = 1}^{C}\left( {{x_{c,1}e_{c,1}} + {x_{c,2}e_{c,2}}} \right)}} & {{Intermediate}{Term}(6)}\end{matrix}$

We define each of the accumulated intensity correction parameters asfollows:

-   -   1) The first accumulated intensity correction parameter x ₁ is        the sum of distribution intensities in the first intensity        channel measured for the target cluster at each of the preceding        sequencing cycles 1 to i−1, and the current sequencing cycle i.    -   2) The second accumulated intensity correction parameter x ₂ is        the sum of distribution intensities in the second intensity        channel measured for the target cluster at each of the preceding        sequencing cycles 1 to i−1, and the current sequencing cycle i.    -   3) The third accumulated intensity correction parameter ē₁ is        the sum of intensity errors in the first intensity channel        calculated for the target cluster at each of the preceding        sequencing cycles 1 to i−1, and the current sequencing cycle i.    -   4) The fourth accumulated intensity correction parameter ē₂ is        the sum of intensity errors in the second intensity channel        calculated for the target cluster at each of the preceding        sequencing cycles 1 to i−1, and the current sequencing cycle i.    -   5) The fifth accumulated intensity correction parameter xx is        the sum of distribution centroid-to-origin distances calculated        for the target cluster at each of the preceding sequencing        cycles 1 to i−1, and the current sequencing cycle i.    -   6) The sixth accumulated intensity correction parameter xe is        the sum of distribution intensity-to-intensity error similarity        measures calculated for the target cluster at each of the        preceding sequencing cycles 1 to i−1, and the current sequencing        cycle i.

The second partial derivative determines a closed-form expression forthe offset coefficients {circumflex over (d)}_(i) as follows:

$\begin{matrix}{\frac{\partial{errorf}}{\partial\hat{a}} = {{\sum\limits_{c = 1}^{C}{\sum\limits_{i = 1}^{2}{2\left( {{\hat{a}x_{C,i}} + {\hat{d}}_{i} - y_{C,i}} \right)}}} = 0}} & {{Equation}(12)}\end{matrix}$ $\begin{matrix}{= {{\sum\limits_{c = 1}^{C}{\sum\limits_{i = 1}^{2}\left( {{\hat{a}x_{C,i}} - x_{C,i} + {\hat{d}}_{i} - y_{C,i} + x_{C,i}} \right)}} = 0}} & {{Equation}(13)}\end{matrix}$ $\begin{matrix}{= {{\sum\limits_{c = 1}^{C}{\sum\limits_{i = 1}^{2}{x_{C,i}^{2}\left( {\left( {\hat{a} - 1} \right) + {\sum\limits_{i = 1}^{2}{\hat{d}}_{i}} + {\sum\limits_{C = 1}^{C}x_{C,i}}} \right)}}} = 0}} & {{Equation}(14)}\end{matrix}$

Then, for each intensity channel:

=C(â−1) x _(i) +C{circumflex over (d)} _(i) −Cē _(i)=0 Equation (15)

=(â−1) x _(i) +{circumflex over (d)} _(i) −ē _(i)=0  Equation (16)

For the first intensity channel, i.e., i=1:

{circumflex over (d)} ₁ =ē ₁+(1−â) x ₁  Equation (17)

where:

-   -   {circumflex over (d)}₁ is the offset coefficient for the first        intensity channel

For the second intensity channel, i.e., i=2:

{circumflex over (d)} ₂ =ē ₂+(1−â) x ₂  Equation (18)

where:

-   -   {circumflex over (d)}₂ is the offset coefficient for the second        intensity channel

Substituting Equations 17 and 18 in Equation 11:

$\begin{matrix}{\frac{\partial{errorf}}{\partial\hat{a}} = {{\left( {a - 1} \right)\overset{\_}{xx}} + {{\overset{\_}{x}}_{i}\left\lbrack {{\overset{\_}{e}}_{1} + {\left( {1 - \hat{a}} \right){\hat{x}}_{1}}} \right\rbrack} +}} & {{Equation}(19)}\end{matrix}$${{{\hat{x}}_{2}\left\lbrack {{\hat{e}}_{2} + {\left( {1 - \hat{a}} \right){\overset{\_}{x}}_{2}}} \right\rbrack} - \overset{\_}{xe}} = 0$$\begin{matrix}{= {{\left( {\hat{a} - 1} \right)\overset{\_}{xx}} + {\left( {1 - \hat{a}} \right){\hat{x}}_{1}^{2}} + {\left( {1 - \hat{a}} \right){\hat{x}}_{2}^{2}} +}} & {{Equation}(20)}\end{matrix}$${{{\overset{\_}{x}}_{1}{\overset{\_}{e}}_{1}} + {{\overset{\_}{x}}_{2}{\overset{\_}{e}}_{2}} - \overset{\_}{xe}} = 0$$\begin{matrix}{= {{{\left( {\hat{a} - 1} \right)\left( {\overset{\_}{xx} - {\hat{x}}_{1}^{2} - {\hat{x}}_{2}^{2}} \right)} + {{\hat{x}}_{1}{\hat{e}}_{1}} + {{\overset{\_}{x}}_{2}{\overset{\_}{e}}_{2}} - \overset{\_}{xe}} = 0}} & {{Equation}(21)}\end{matrix}$ $\begin{matrix}{{\hat{a} - 1} = \frac{{{\overset{\_}{x}}_{1}{\overset{\_}{e}}_{1}} + {{\overset{\_}{x}}_{2}{\overset{\_}{e}}_{2}} - \overset{\_}{xe}}{\overset{\_}{xx} - {\overset{\_}{x}}_{1}^{2} - {\overset{\_}{x}}_{2}^{2}}} & {{Equation}(22)}\end{matrix}$ $\begin{matrix}{\hat{a} = {1 + \frac{{{\overset{\_}{x}}_{1}{\overset{\_}{e}}_{1}} + {{\overset{\_}{x}}_{2}{\overset{\_}{e}}_{2}} - \overset{\_}{xe}}{\overset{\_}{xx} - {\overset{\_}{x}}_{1}^{2} - {\overset{\_}{x}}_{2}^{2}}}} & {{Equation}(23)}\end{matrix}$

where:

-   -   â is the amplification coefficient for the target cluster

In another implementation, to reduce the memory requirements percluster, the common offset coefficient for the different intensitychannels (e.g., the first and the second intensity channels) isdetermined as follows by introducing the constraint {circumflex over(d)}₁={circumflex over (d)}₂:

$\begin{matrix}{\overset{\_}{x} = {\frac{1}{2C}{\sum\limits_{c = 1}^{C}\left( {x_{C,1} + x_{C,2}} \right)}}} & {{Intermediate}{Term}(1.1)}\end{matrix}$ $\begin{matrix}{\overset{\_}{xx} = {\frac{1}{C}{\sum\limits_{c = 1}^{C}\left( {x_{C,1}^{2} + x_{C,2}^{2}} \right)}}} & {{Intermediate}{Term}(2.1)}\end{matrix}$ $\begin{matrix}{\overset{\_}{e} = {\frac{1}{2C}{\sum\limits_{c = 1}^{C}\left( {e_{C,1} + e_{C,2}} \right)}}} & {{Intermediate}{Term}(3.1)}\end{matrix}$ $\begin{matrix}{\overset{\_}{xe} = {\frac{1}{C}{\sum\limits_{c = 1}^{C}\left( {{x_{C,1}e_{C,1}} + {x_{C,2}e_{C,2}}} \right)}}} & {{Intermediate}{Term}(4.1)}\end{matrix}$ $\begin{matrix}{\hat{a} = {1 + \frac{\overset{\_}{xe} + {2\overset{\_}{x}\overset{\_}{e}}}{\overset{\_}{xx} - {2{\overset{\_}{x}}^{2}}}}} & {{Equation}(24)}\end{matrix}$ $\begin{matrix}{{\hat{d}}_{1} = {\overset{\_}{e} + {\overset{\_}{x}\left( {1 - \hat{a}} \right)}}} & {{Equation}(25)}\end{matrix}$ $\begin{matrix}{{\hat{d}}_{2} = {\overset{\_}{e} + {\overset{\_}{x}\left( {1 - \hat{a}} \right)}}} & {{Equation}(26)}\end{matrix}$

It would be apparent to one skilled in the art that the least-squaressolution is executed in advance of the sequencing run to determine theclosed-form expressions. Once determined, the closed-form expressionsare applied to the intensity values generated during the sequencing runon a cluster-by-cluster and iteratively at each sequencing cycle of thesequencing run.

Additional details about the per-cluster variation correctioncoefficients and how they are determined can be found in U.S.Provisional Patent Application No. 63/106,256, titled “SYSTEMS ANDMETHODS FOR PER-CLUSTER INTENSITY CORRECTION AND BASE CALLING,” filed 27Oct. 2021 (Attorney Docket No. ILLM 1034-1/IP-2026-PRV), which isincorporated herein by reference in its entirety.

In FIG. 34 , the per-cluster states 3042 are the per-cluster variationcorrection coefficients determined by the RTA for the current sequencingcycle, in accordance with one implementation. Then, the per-clusterstates 3042 are combined with spatially convolved features of thecluster pixels and of the current base calling iteration/operation togenerate base calls for the corresponding clusters. These steps areillustrated by actions 3402, 3412, 3422, and 3432 of FIG. 34 .

Compression Network

As discussed above, the specialized architecture of the neuralnetwork-based base caller 2900 processes sliding windows of imagepatches for corresponding sequencing cycles. Overlap exists betweensequencing cycles of successive sliding windows. This causes the neuralnetwork-based base caller 2900 to redundantly process image patches forthe overlapping sequencing cycles. This in turn results in waste ofcompute resources. For example, in one implementation, each spatialconvolution layer of the neural network-based base caller 2900 hasnearly 100 million multiplication operations. Then, for a window of fivesequencing cycles and a cascade (or sequence) of seven spatialconvolution layers, the spatial convolution neural network executesabout 620 million multiplication operations. Furthermore, the temporalconvolution neural network executes about 10 million multiplicationoperations.

Since the image data for cycle N−1 in a current sliding window (or acurrent iteration of base calling) is processed as cycle N in theprevious sliding window (or a previous iteration of base calling), anopportunity arises to store the intermediate results of the processingdone in the current sliding window and the intermediate results them insubsequent sliding windows, and thereby bypass (or obviate) redundantprocessing (or reprocessing) of input image data for overlappingsequencing cycles between successive sliding windows.

However, the intermediate results are several terabytes of data thatrequire impractical amount of storage. To overcome this technicalproblem, the technology disclosed proposes compressing the intermediateresults the first time the intermediate results are generated by theneural network-based base caller 2900 and repurposing the compressedintermediate results in subsequent sliding windows to avoid redundantcomputation, and thereby not regenerating (or only-once generating) theintermediate results.

In FIG. 35 , a compression logic 3530 (or compression network orcompression subnetwork or compression layer or squeezing layer)processes the outputs of the cluster feature filtering logic 3030 andgenerates a compressed representation of the outputs, in accordance withone implementation. In one implementation, the compression network 3530comprises a compression convolution layer that reduces the depthdimensionality of feature maps generated by the cluster featurefiltering logic 3030.

For example, consider that the depth dimensionality of the filtered,per-cluster spatial maps 3054, 3064, 3074, 3084, and 3094 is 14 (i.e.,fourteen feature maps or fourteen channels per spatial output). Thecompression network 3530 attenuates the filtered, per-cluster spatialmaps 3054, 3064, 3074, 3084, and 3094 into respective compressedfiltered, per-cluster spatial maps 3554, 3564, 3574, 3584, and 3594 forthe respective sequencing cycles N−2, N−1, N, N+1, and N+2, collectivelyreferred to as compressed cluster features 3504. In one implementation,each of the compressed filtered, per-cluster spatial maps 3554, 3564,3574, 3584, and 3594 has a depth dimensionality of 2 (i.e., two featuremaps or two channels per compressed spatial output). In otherimplementations, the compressed filtered, per-cluster spatial maps 3554,3564, 3574, 3584, and 3594 can have a depth dimensionality of 3 or 4(i.e., three or fourth feature maps or three or fourth channels percompressed spatial output). In yet other implementations, the compressedfiltered, per-cluster spatial maps 3554, 3564, 3574, 3584, and 3594 canhave a depth dimensionality of 1 (i.e., one feature map or one channelper compressed spatial output). In one implementation, the compressionlayer 3530 does not include an activation function like ReLU. In otherimplementations, it can include an activation function. In otherimplementations, the compression logic 3530 can configure thecorresponding compressed spatial map sets to each have more than fourfeature maps.

The discussion now turns to how the compression logic 3530 generates thecompressed outputs.

In one implementation, the compression logic 3530 uses 1×1 convolutionsto reduce the number of feature maps (i.e., the depth dimension or thenumber of channels) while introducing non-linearity. The 1×1convolutions have a kernel size of 1. The 1×1 convolutions can transforma volume depth into another squeezed or expanded representation withoutchanging the spatial dimensions. A 1×1 convolution operates like a fullyconnected linear layer across the input channels. This is useful inmapping from feature maps with many channels to fewer feature maps. Asingle 1×1 convolution can be applied to an input tensor with twofeature maps. The 1×1 convolution compresses the two-channel input to asingle-channel output.

The number of compressed outputs (or compressed feature maps orcompressed spatial maps or compressed temporal maps) generated by thecompression layer 108 is a function of the number of 1×1 convolutionfilters (or compression convolution filters or compression filters)configured in the compression layer 3530. In one implementation, thecompression layer 3530 can have two 1×1 convolution filters. The first1×1 convolution filter can process a spatial feature volume with thefourteen feature maps and generate a first feature map while preservingthe spatial dimensionality of 101×101. The second 1×1 convolution filtercan also process the spatial feature volume with the fourteen featuremaps and generate a second feature map while preserving the spatialdimensionality of 101×101. Accordingly, the compression layer 3530reduces the spatial feature volume with the fourteen feature maps into acompressed output with two spatial feature maps (i.e., compressionratio=7).

In some implementations, the technology disclosed saves about 80% ofconvolutions in the spatial network of the neural network-based basecaller 2900. In one implementation, the 80% savings are observed in thespatial convolutions when a compression logic and repurposing of thecompressed feature maps in subsequent sequencing cycles is used for aninput window of five sequencing cycles (e.g., cycle N, cycle N+1, cycleN−1, cycle N+2, and cycle N−2). In another implementation, 90% savingsare observed in the spatial convolutions when the compression logic andrepurposing of the compressed feature maps in subsequent sequencingcycles is used for an input window of ten sequencing cycles (e.g., cycleN, cycle N+1, cycle N−1, cycle N+2, cycle N−2, cycle N+3, and cycleN−3). That is, the larger the window size, the bigger the savings fromthe use of the compression logic and repurposing of the compressedfeature maps, and the larger the window size, the better the basecalling performance due to incorporation of greater context fromadditional flanking cycles. So bigger savings for bigger windowsimproves overall performance for a given compute capability.

The compute efficiency and compact compute footprint brought about bythe compression logic facilitates hardware implementation of the neuralnetwork-based base caller 2900 on resource-constrained processors likeCentral Processing Units (CPUs), Graphics Processing Units (GPUs), FieldProgrammable Gate Arrays (FPGAs), Coarse-Grained ReconfigurableArchitectures (CGRAs), Application-Specific Integrated Circuits (ASICs),Application Specific Instruction-set Processor (ASIP), and DigitalSignal Processors (DSPs).

The compute saved by the compression logic allows for incorporating moreconvolution operators in the neural network-based base caller 2900.Examples include adding more convolution filters in the spatial andtemporal convolution layers, increasing the size of the convolutionfilters, and increasing the number of spatial and temporal convolutionlayers. Additional convolution operations improve intensity patterndetection and overall base calling accuracy of the neural network-basedbase caller 2900.

Additional details about the compression logic and its compressedoutputs can be found in U.S. Nonprovisional patent application Ser. No.17/179,395, titled “DATA COMPRESSION FOR ARTIFICIAL INTELLIGENCE-BASEDBASE CALLING,” filed 18 Feb. 2021 (Attorney Docket No. ILLM1029-2/IP-1964-US), which is incorporated herein by reference in itsentirety.

In FIG. 35 , per-cluster states 3042 for the current sequencing cycle Nare combined with intermediate outputs of the neural network-based basecaller 2900, i.e., the compressed filtered, per-cluster spatial maps3554, 3564, 3574, 3584, and 3594.

In some implementations, the dimensionality of the per-cluster states3042 is modified to conform with the dimensionality of the compressedfiltered, per-cluster spatial maps 3554, 3564, 3574, 3584, and 3594(e.g., by trimming dimensions, adding dimensions, padding (e.g., zeropadding), cloning, etc.). In different implementations, the respectivefive instances of the per-cluster states 3042 and the compressedfiltered, per-cluster spatial maps 3554, 3564, 3574, 3584, and 3594 canbe respectively combined using techniques discussed above, such asconcatenation, summation, element-wise multiplication, element-wisemultiplication and summation (convolution), and so on.

Then, according to the illustrated example in FIG. 35 , the respectivefive combinations of the respective compressed filtered, per-clusterspatial maps 3554, 3564, 3574, 3584, and 3594 and the respective fiveinstances of the per-cluster states 3042 are processed by the temporallogic 2928 to generate base calls 3080 for the current sequencing cycleN. The base calls 3080 are made for clusters corresponding to thecluster pixels and therefore the cluster features 3004.

The discussion now turns to different variations of the per-clusterstates 3042 and how these variations are determined according todifferent implementations of the technology disclosed.

FIG. 36 illustrates one implementation of generating 3600 per-clusterstates using historic feature values of compressed spatially convolvedfeatures corresponding to cluster pixels. In FIG. 36 , the per-clusterstates 3042 are determined by accumulating summary statistics ofhistoric feature values of the compressed spatially convolved featurescorresponding to the cluster pixels from the previous sequencing cyclesand the current sequencing cycle, as discussed above, for example, usingminimum value selection function, maximum value selection function,averaging function, exponential weighted averaging function, and so on.Then, the per-cluster states 3042 are combined with compressed spatiallyconvolved features of the cluster pixels and of the current base callingiteration/operation to generate base calls for the correspondingclusters. These steps are illustrated by actions 3602, 3612, 3622, 3632,and 3642 of FIG. 36 .

In FIG. 37 , the per-cluster states 3042 are the per-cluster variationcorrection coefficients determined by the RTA for the current sequencingcycle, in accordance with one implementation. Then, the per-clusterstates 3042 are combined with compressed spatially convolved features ofthe cluster pixels and of the current base calling iteration/operationto generate base calls for the corresponding clusters. These steps areillustrated by actions 3702, 3712, 3722, 3732, and 3742 of FIG. 37 .

FIG. 38 illustrates one implementation of generating 3800 per-clusterstates 3042 using historic intensity values of corresponding clusterpixels for incorporation of the per-cluster states 3042 with compressedfeatures. In FIG. 38 , the per-cluster states 3042 are determined byaccumulating summary statistics of historic intensity values of thecluster pixels from the previous sequencing cycles and the currentsequencing cycle, as discussed above, for example, using minimum valueselection function, maximum value selection function, averagingfunction, exponential weighted averaging function, and so on. Then, theper-cluster states 3042 are combined with compressed spatially convolvedfeatures of the cluster pixels and of a current base callingiteration/operation to generate base calls for the correspondingclusters. These steps are illustrated by actions 3802, 3812, 3822, 3832,and 3842 of FIG. 38 .

Well State Data to Pixel State Data Transformation

FIG. 39 illustrates one implementation of generating dense, per-pixelstates from sparse, per-well states. In FIG. 39 , the per-well states3902 are sparse and can be transformed into dense, per-pixels states3906 using transpose convolutions or interpolation (e.g., linear,bilinear, cubic interpolation) 3904.

“Sparse” and “dense” refer to the number of zero versus non-zeroelements in an array (e.g., vector or matrix). A sparse array is onethat contains mostly zeros and few non-zero entries. A dense arraycontains mostly non-zeros. In a dense array, in one implementation, eachper-pixel state can be surrounded by at least two, four, or eightneighboring per-pixel states. In a sparse array, in one implementation,each per-well state is not surrounded by even two, four, or eightneighboring per-well states. A collection of states is called dense ifall rows between the first row and the last row are defined withnon-zero elements and given a value. A collection of states is calledsparse if there are gaps or zero elements in the rows. Sparseness canmean that many elements (e.g., every other, every third, or everyfourth) are zero or very close to zero.

FIG. 40 illustrates one implementation of providing the sparse, per-wellstates, the dense, per-pixel states, and per-pixel intensity values asinput to the base caller for executing base calling operations. In FIG.40 , the sparse, per-well states 3902 can be provided as input to thebase caller 144, e.g., the neural network-based base caller 2900, as adirect input as well as redundant inputs using, for example, residualconnections and/or skip connections. In some implementations, thesparse, per-well states 3902 are provided as supplemental input alongwith the dense, per-pixel states 3906 and dense per-pixel intensityvalues 4012 (i.e., cluster images), which are processed by the basecaller 144 to generate base calls.

FIG. 41 illustrates one implementation of using the sparse, per-wellstates, the dense, per-pixel states, and per-pixel intensity values asinput to the neural network-based base caller 2900 for executing basecalling operations. The input to the neural network-based base caller2900 are the dense, per-pixel states 3906 and the dense per-pixelintensity values 4012 (i.e., cluster images).

Sequencing System

FIGS. 42A and 42B depict one implementation of a sequencing system4200A. The sequencing system 4200A comprises a configurable processor4246. The configurable processor 4246 implements the base callingtechniques disclosed herein. The sequencing system is also referred toas a “sequencer.”

The sequencing system 4200A can operate to obtain any information ordata that relates to at least one of a biological or chemical substance.In some implementations, the sequencing system 4200A is a workstationthat may be similar to a bench-top device or desktop computer. Forexample, a majority (or all) of the systems and components forconducting the desired reactions can be within a common housing 4202.

In particular implementations, the sequencing system 4200A is a nucleicacid sequencing system configured for various applications, includingbut not limited to de novo sequencing, resequencing of whole genomes ortarget genomic regions, and metagenomics. The sequencer may also be usedfor DNA or RNA analysis. In some implementations, the sequencing system4200A may also be configured to generate reaction sites in a biosensor.For example, the sequencing system 4200A may be configured to receive asample and generate surface attached clusters of clonally amplifiednucleic acids derived from the sample. Each cluster may constitute or bepart of a reaction site in the biosensor.

The exemplary sequencing system 4200A may include a system receptacle orinterface 4210 that is configured to interact with a biosensor 4212 toperform desired reactions within the biosensor 4212. In the followingdescription with respect to FIG. 42A, the biosensor 4212 is loaded intothe system receptacle 4210. However, it is understood that a cartridgethat includes the biosensor 4212 may be inserted into the systemreceptacle 4210 and in some states the cartridge can be removedtemporarily or permanently. As described above, the cartridge mayinclude, among other things, fluidic control and fluidic storagecomponents.

In particular implementations, the sequencing system 4200A is configuredto perform a large number of parallel reactions within the biosensor4212. The biosensor 4212 includes one or more reaction sites wheredesired reactions can occur. The reaction sites may be, for example,immobilized to a solid surface of the biosensor or immobilized to beads(or other movable substrates) that are located within correspondingreaction chambers of the biosensor. The reaction sites can include, forexample, clusters of clonally amplified nucleic acids. The biosensor4212 may include a solid-state imaging device (e.g., CCD or CMOS imager)and a flow cell mounted thereto. The flow cell may include one or moreflow channels that receive a solution from the sequencing system 4200Aand direct the solution toward the reaction sites. Optionally, thebiosensor 4212 can be configured to engage a thermal element fortransferring thermal energy into or out of the flow channel.

The sequencing system 4200A may include various components, assemblies,and systems (or sub-systems) that interact with each other to perform apredetermined method or assay protocol for biological or chemicalanalysis. For example, the sequencing system 4200A includes a systemcontroller 4206 that may communicate with the various components,assemblies, and sub-systems of the sequencing system 4200A and also thebiosensor 4212. For example, in addition to the system receptacle 4210,the sequencing system 4200A may also include a fluidic control system4208 to control the flow of fluid throughout a fluid network of thesequencing system 4200A and the biosensor 4212; a fluid storage system4214 that is configured to hold all fluids (e.g., gas or liquids) thatmay be used by the bioassay system; a temperature control system 4204that may regulate the temperature of the fluid in the fluid network, thefluid storage system 4214, and/or the biosensor 4212; and anillumination system 4216 that is configured to illuminate the biosensor4212. As described above, if a cartridge having the biosensor 4212 isloaded into the system receptacle 4210, the cartridge may also includefluidic control and fluidic storage components.

Also shown, the sequencing system 4200A may include a user interface4218 that interacts with the user. For example, the user interface 4218may include a display 4220 to display or request information from a userand a user input device 4222 to receive user inputs. In someimplementations, the display 4220 and the user input device 4222 are thesame device. For example, the user interface 4218 may include atouch-sensitive display configured to detect the presence of anindividual's touch and also identify a location of the touch on thedisplay. However, other user input devices 4222 may be used, such as amouse, touchpad, keyboard, keypad, handheld scanner, voice-recognitionsystem, motion-recognition system, and the like. As will be discussed ingreater detail below, the sequencing system 4200A may communicate withvarious components, including the biosensor 4212 (e.g., in the form of acartridge), to perform the desired reactions. The sequencing system4200A may also be configured to analyze data obtained from the biosensorto provide a user with desired information.

The system controller 4206 may include any processor-based ormicroprocessor-based system, including systems using microcontrollers,reduced instruction set computers (RISC), application specificintegrated circuits (ASICs), field programmable gate array (FPGAs),coarse-grained reconfigurable architectures (CGRAs), logic circuits, andany other circuit or processor capable of executing functions describedherein. The above examples are exemplary only, and are thus not intendedto limit in any way the definition and/or meaning of the term systemcontroller. In the exemplary implementation, the system controller 4206executes a set of instructions that are stored in one or more storageelements, memories, or modules in order to at least one of obtain andanalyze detection data. Detection data can include a plurality ofsequences of pixel signals, such that a sequence of pixel signals fromeach of the millions of sensors (or pixels) can be detected over manybase calling cycles. Storage elements may be in the form of informationsources or physical memory elements within the sequencing system 4200A.

The set of instructions may include various commands that instruct thesequencing system 4200A or biosensor 4212 to perform specific operationssuch as the methods and processes of the various implementationsdescribed herein. The set of instructions may be in the form of asoftware program, which may form part of a tangible, non-transitorycomputer readable medium or media. As used herein, the terms “software”and “firmware” are interchangeable, and include any computer programstored in memory for execution by a computer, including RAM memory, ROMmemory, EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM)memory. The above memory types are exemplary only, and are thus notlimiting as to the types of memory usable for storage of a computerprogram.

The software may be in various forms such as system software orapplication software. Further, the software may be in the form of acollection of separate programs, or a program module within a largerprogram or a portion of a program module. The software also may includemodular programming in the form of object-oriented programming. Afterobtaining the detection data, the detection data may be automaticallyprocessed by the sequencing system 4200A, processed in response to userinputs, or processed in response to a request made by another processingmachine (e.g., a remote request through a communication link). In theillustrated implementation, the system controller 4206 includes ananalysis module 4244. In other implementations, system controller 4206does not include the analysis module 4244 and instead has access to theanalysis module 4244 (e.g., the analysis module 4244 may be separatelyhosted on cloud).

The system controller 4206 may be connected to the biosensor 4212 andthe other components of the sequencing system 4200A via communicationlinks. The system controller 4206 may also be communicatively connectedto off-site systems or servers. The communication links may behardwired, corded, or wireless. The system controller 4206 may receiveuser inputs or commands, from the user interface 4218 and the user inputdevice 4222.

The fluidic control system 4208 includes a fluid network and isconfigured to direct and regulate the flow of one or more fluids throughthe fluid network. The fluid network may be in fluid communication withthe biosensor 4212 and the fluid storage system 4214. For example,select fluids may be drawn from the fluid storage system 4214 anddirected to the biosensor 4212 in a controlled manner, or the fluids maybe drawn from the biosensor 4212 and directed toward, for example, awaste reservoir in the fluid storage system 4214. Although not shown,the fluidic control system 4208 may include flow sensors that detect aflow rate or pressure of the fluids within the fluid network. Thesensors may communicate with the system controller 4206.

The temperature control system 4204 is configured to regulate thetemperature of fluids at different regions of the fluid network, thefluid storage system 4214, and/or the biosensor 4212. For example, thetemperature control system 4204 may include a thermocycler thatinterfaces with the biosensor 4212 and controls the temperature of thefluid that flows along the reaction sites in the biosensor 4212. Thetemperature control system 4204 may also regulate the temperature ofsolid elements or components of the sequencing system 4200A or thebiosensor 4212. Although not shown, the temperature control system 4204may include sensors to detect the temperature of the fluid or othercomponents. The sensors may communicate with the system controller 4206.

The fluid storage system 4214 is in fluid communication with thebiosensor 4212 and may store various reaction components or reactantsthat are used to conduct the desired reactions therein. The fluidstorage system 4214 may also store fluids for washing or cleaning thefluid network and biosensor 4212 and for diluting the reactants. Forexample, the fluid storage system 4214 may include various reservoirs tostore samples, reagents, enzymes, other biomolecules, buffer solutions,aqueous, and non-polar solutions, and the like. Furthermore, the fluidstorage system 4214 may also include waste reservoirs for receivingwaste products from the biosensor 4212. In implementations that includea cartridge, the cartridge may include one or more of a fluid storagesystem, fluidic control system or temperature control system.Accordingly, one or more of the components set forth herein as relatingto those systems can be contained within a cartridge housing. Forexample, a cartridge can have various reservoirs to store samples,reagents, enzymes, other biomolecules, buffer solutions, aqueous, andnon-polar solutions, waste, and the like. As such, one or more of afluid storage system, fluidic control system or temperature controlsystem can be removably engaged with a bioassay system via a cartridgeor other biosensor.

The illumination system 4216 may include a light source (e.g., one ormore LEDs) and a plurality of optical components to illuminate thebiosensor. Examples of light sources may include lasers, arc lamps,LEDs, or laser diodes. The optical components may be, for example,reflectors, dichroics, beam splitters, collimators, lenses, filters,wedges, prisms, mirrors, detectors, and the like. In implementationsthat use an illumination system, the illumination system 4216 may beconfigured to direct an excitation light to reaction sites. As oneexample, fluorophores may be excited by green wavelengths of light, assuch the wavelength of the excitation light may be approximately 4232nm. In one implementation, the illumination system 4216 is configured toproduce illumination that is parallel to a surface normal of a surfaceof the biosensor 4212. In another implementation, the illuminationsystem 4216 is configured to produce illumination that is off-anglerelative to the surface normal of the surface of the biosensor 4212. Inyet another implementation, the illumination system 4216 is configuredto produce illumination that has plural angles, including some parallelillumination and some off-angle illumination.

The system receptacle or interface 4210 is configured to engage thebiosensor 4212 in at least one of a mechanical, electrical, and fluidicmanner. The system receptacle 4210 may hold the biosensor 4212 in adesired orientation to facilitate the flow of fluid through thebiosensor 4212. The system receptacle 4210 may also include electricalcontacts that are configured to engage the biosensor 4212 so that thesequencing system 4200A may communicate with the biosensor 4212 and/orprovide power to the biosensor 4212. Furthermore, the system receptacle4210 may include fluidic ports (e.g., nozzles) that are configured toengage the biosensor 4212. In some implementations, the biosensor 4212is removably coupled to the system receptacle 4210 in a mechanicalmanner, in an electrical manner, and also in a fluidic manner.

In addition, the sequencing system 4200A may communicate remotely withother systems or networks or with other bioassay systems 4200A.Detection data obtained by the bioassay system(s) 4200A may be stored ina remote database.

FIG. 42B is a block diagram of a system controller 4206 that can be usedin the system of FIG. 42A. In one implementation, the system controller4206 includes one or more processors or modules that can communicatewith one another. Each of the processors or modules may include analgorithm (e.g., instructions stored on a tangible and/or non-transitorycomputer readable storage medium) or sub-algorithms to performparticular processes. The system controller 4206 is illustratedconceptually as a collection of modules, but may be implementedutilizing any combination of dedicated hardware boards, DSPs,processors, etc. Alternatively, the system controller 4206 may beimplemented utilizing an off-the-shelf PC with a single processor ormultiple processors, with the functional operations distributed betweenthe processors. As a further option, the modules described below may beimplemented utilizing a hybrid configuration in which certain modularfunctions are performed utilizing dedicated hardware, while theremaining modular functions are performed utilizing an off-the-shelf PCand the like. The modules also may be implemented as software moduleswithin a processing unit.

During operation, a communication port 4250 may transmit information(e.g., commands) to or receive information (e.g., data) from thebiosensor 4212 (FIG. 42A) and/or the sub-systems 4208, 4214, 4204 (FIG.42A). In implementations, the communication port 4250 may output aplurality of sequences of pixel signals. A communication link 4234 mayreceive user input from the user interface 4218 (FIG. 42A) and transmitdata or information to the user interface 4218. Data from the biosensor4212 or sub-systems 4208, 4214, 4204 may be processed by the systemcontroller 4206 in real-time during a bioassay session. Additionally oralternatively, data may be stored temporarily in a system memory duringa bioassay session and processed in slower than real-time or off-lineoperation.

As shown in FIG. 42B, the system controller 4206 may include a pluralityof modules 4226-4248 that communicate with a main control module 4224,along with a central processing unit (CPU) 4252. The main control module4224 may communicate with the user interface 4218 (FIG. 42A). Althoughthe modules 4226-4248 are shown as communicating directly with the maincontrol module 4224, the modules 4226-4248 may also communicate directlywith each other, the user interface 4218, and the biosensor 4212. Also,the modules 4226-4248 may communicate with the main control module 4224through the other modules.

The plurality of modules 4226-4248 include system modules 4228-4232,4226 that communicate with the sub-systems 4208, 4214, 4204, and 4216,respectively. The fluidic control module 4228 may communicate with thefluidic control system 4208 to control the valves and flow sensors ofthe fluid network for controlling the flow of one or more fluids throughthe fluid network. The fluid storage module 4230 may notify the userwhen fluids are low or when the waste reservoir is at or near capacity.The fluid storage module 4230 may also communicate with the temperaturecontrol module 4232 so that the fluids may be stored at a desiredtemperature. The illumination module 4226 may communicate with theillumination system 4216 to illuminate the reaction sites at designatedtimes during a protocol, such as after the desired reactions (e.g.,binding events) have occurred. In some implementations, the illuminationmodule 4226 may communicate with the illumination system 4216 toilluminate the reaction sites at designated angles.

The plurality of modules 4226-4248 may also include a device module 4236that communicates with the biosensor 4212 and an identification module4238 that determines identification information relating to thebiosensor 4212. The device module 4236 may, for example, communicatewith the system receptacle 4210 to confirm that the biosensor hasestablished an electrical and fluidic connection with the sequencingsystem 4200A. The identification module 4238 may receive signals thatidentify the biosensor 4212. The identification module 4238 may use theidentity of the biosensor 4212 to provide other information to the user.For example, the identification module 4238 may determine and thendisplay a lot number, a date of manufacture, or a protocol that isrecommended to be run with the biosensor 4212.

The plurality of modules 4226-4248 also includes an analysis module 4244(also called signal processing module or signal processor) that receivesand analyzes the signal data (e.g., image data) from the biosensor 4212.Analysis module 4244 includes memory (e.g., RAM or Flash) to storedetection/image data. Detection data can include a plurality ofsequences of pixel signals, such that a sequence of pixel signals fromeach of the millions of sensors (or pixels) can be detected over manybase calling cycles. The signal data may be stored for subsequentanalysis or may be transmitted to the user interface 4218 to displaydesired information to the user. In some implementations, the signaldata may be processed by the solid-state imager (e.g., CMOS imagesensor) before the analysis module 4244 receives the signal data.

The analysis module 4244 is configured to obtain image data from thelight detectors at each of a plurality of sequencing cycles. The imagedata is derived from the emission signals detected by the lightdetectors and process the image data for each of the plurality ofsequencing cycles through the neural network-based base caller 2900 andproduce a base call for at least some of the clusters at each of theplurality of sequencing cycle. The light detectors can be part of one ormore over-head cameras (e.g., Illumina's GAIIx's CCD camera takingimages of the clusters on the biosensor 4212 from the top), or can bepart of the biosensor 4212 itself (e.g., Illumina's iSeq's CMOS imagesensors underlying the clusters on the biosensor 4212 and taking imagesof the clusters from the bottom).

The output of the light detectors is the sequencing images, eachdepicting intensity emissions of the clusters and their surroundingbackground. The sequencing images depict intensity emissions generatedas a result of nucleotide incorporation in the sequences during thesequencing. The intensity emissions are from associated clusters andtheir surrounding background. The sequencing images are stored in memory4248.

Protocol modules 4240 and 4242 communicate with the main control module4224 to control the operation of the sub-systems 4208, 4214, and 4204when conducting predetermined assay protocols. The protocol modules 4240and 4242 may include sets of instructions for instructing the sequencingsystem 4200A to perform specific operations pursuant to predeterminedprotocols. As shown, the protocol module may be asequencing-by-synthesis (SBS) module 4240 that is configured to issuevarious commands for performing sequencing-by-synthesis processes. InSBS, extension of a nucleic acid primer along a nucleic acid template ismonitored to determine the sequence of nucleotides in the template. Theunderlying chemical process can be polymerization (e.g., as catalyzed bya polymerase enzyme) or ligation (e.g., catalyzed by a ligase enzyme).In a particular polymerase-based SBS implementation, fluorescentlylabeled nucleotides are added to a primer (thereby extending the primer)in a template dependent fashion such that detection of the order andtype of nucleotides added to the primer can be used to determine thesequence of the template. For example, to initiate a first SBS cycle,commands can be given to deliver one or more labeled nucleotides, DNApolymerase, etc., into/through a flow cell that houses an array ofnucleic acid templates. The nucleic acid templates may be located atcorresponding reaction sites. Those reaction sites where primerextension causes a labeled nucleotide to be incorporated can be detectedthrough an imaging event. During an imaging event, the illuminationsystem 4216 may provide an excitation light to the reaction sites.Optionally, the nucleotides can further include a reversible terminationproperty that terminates further primer extension once a nucleotide hasbeen added to a primer. For example, a nucleotide analog having areversible terminator moiety can be added to a primer such thatsubsequent extension cannot occur until a deblocking agent is deliveredto remove the moiety. Thus, for implementations that use reversibletermination a command can be given to deliver a deblocking reagent tothe flow cell (before or after detection occurs). One or more commandscan be given to effect wash(es) between the various delivery steps. Thecycle can then be repeated n times to extend the primer by nnucleotides, thereby detecting a sequence of length n. Exemplarysequencing techniques are described, for example, in Bentley et al.,Nature 456:53-59 (2008); WO 04/018497; U.S. Pat. No. 7,057,026; WO91/06678; WO 07/123744; U.S. Pat. Nos. 7,329,492; 7,211,414; 7,315,019;7,405,281, and US 2008/014708082, each of which is incorporated hereinby reference.

For the nucleotide delivery step of an SBS cycle, either a single typeof nucleotide can be delivered at a time, or multiple differentnucleotide types (e.g., A, C, T and G together) can be delivered. For anucleotide delivery configuration where only a single type of nucleotideis present at a time, the different nucleotides need not have distinctlabels since they can be distinguished based on temporal separationinherent in the individualized delivery. Accordingly, a sequencingmethod or apparatus can use single color detection. For example, anexcitation source need only provide excitation at a single wavelength orin a single range of wavelengths. For a nucleotide deliveryconfiguration where delivery results in multiple different nucleotidesbeing present in the flow cell at one time, sites that incorporatedifferent nucleotide types can be distinguished based on differentfluorescent labels that are attached to respective nucleotide types inthe mixture. For example, four different nucleotides can be used, eachhaving one of four different fluorophores. In one implementation, thefour different fluorophores can be distinguished using excitation infour different regions of the spectrum. For example, four differentexcitation radiation sources can be used. Alternatively, fewer than fourdifferent excitation sources can be used, but optical filtration of theexcitation radiation from a single source can be used to producedifferent ranges of excitation radiation at the flow cell.

In some implementations, fewer than four different colors can bedetected in a mixture having four different nucleotides. For example,pairs of nucleotides can be detected at the same wavelength, butdistinguished based on a difference in intensity for one member of thepair compared to the other, or based on a change to one member of thepair (e.g., via chemical modification, photochemical modification orphysical modification) that causes apparent signal to appear ordisappear compared to the signal detected for the other member of thepair. Exemplary apparatus and methods for distinguishing four differentnucleotides using detection of fewer than four colors are described forexample in US Pat. App. Ser. Nos. 61/538,294 and 61/619,878, which areincorporated herein by reference in their entireties. U.S. applicationSer. No. 13/624,200, which was filed on Sep. 21, 2012, is alsoincorporated by reference in its entirety.

The plurality of protocol modules may also include a sample-preparation(or generation) module 4242 that is configured to issue commands to thefluidic control system 4208 and the temperature control system 4204 foramplifying a product within the biosensor 4212. For example, thebiosensor 4212 may be engaged to the sequencing system 4200A. Theamplification module 4242 may issue instructions to the fluidic controlsystem 4208 to deliver necessary amplification components to reactionchambers within the biosensor 4212. In other implementations, thereaction sites may already contain some components for amplification,such as the template DNA and/or primers. After delivering theamplification components to the reaction chambers, the amplificationmodule 4242 may instruct the temperature control system 4204 to cyclethrough different temperature stages according to known amplificationprotocols. In some implementations, the amplification and/or nucleotideincorporation is performed isothermally.

The SBS module 4240 may issue commands to perform bridge PCR whereclusters of clonal amplicons are formed on localized areas within achannel of a flow cell. After generating the amplicons through bridgePCR, the amplicons may be “linearized” to make single stranded templateDNA, or sstDNA, and a sequencing primer may be hybridized to a universalsequence that flanks a region of interest. For example, a reversibleterminator-based sequencing by synthesis method can be used as set forthabove or as follows.

Each base calling or sequencing cycle can extend an sstDNA by a singlebase which can be accomplished for example by using a modified DNApolymerase and a mixture of four types of nucleotides. The differenttypes of nucleotides can have unique fluorescent labels, and eachnucleotide can further have a reversible terminator that allows only asingle-base incorporation to occur in each cycle. After a single base isadded to the sstDNA, excitation light may be incident upon the reactionsites and fluorescent emissions may be detected. After detection, thefluorescent label and the terminator may be chemically cleaved from thesstDNA. Another similar base calling or sequencing cycle may follow. Insuch a sequencing protocol, the SBS module 4240 may instruct the fluidiccontrol system 4208 to direct a flow of reagent and enzyme solutionsthrough the biosensor 4212. Exemplary reversible terminator-based SBSmethods which can be utilized with the apparatus and methods set forthherein are described in US Patent Application Publication No.2007/0166705 A1, US Patent Application Publication No. 2006/0188901 A1,U.S. Pat. No. 7,057,026, US Patent Application Publication No.2006/0240439 A1, US Patent Application Publication No. 2006/02814714709Al, PCT Publication No. WO 05/065814, US Patent Application PublicationNo. 2005/014700900 Al, PCT Publication No. WO 06/08B199 and PCTPublication No. WO 07/01470251, each of which is incorporated herein byreference in its entirety. Exemplary reagents for reversibleterminator-based SBS are described in U.S. Pat. Nos. 7,541,444;7,057,026; 7,414,14716; U.S. Pat. Nos. 7,427,673; 7,566,537; 7,592,435and WO 07/14835368, each of which is incorporated herein by reference inits entirety.

In some implementations, the amplification and SBS modules may operatein a single assay protocol where, for example, template nucleic acid isamplified and subsequently sequenced within the same cartridge.

The sequencing system 4200A may also allow the user to reconfigure anassay protocol. For example, the sequencing system 4200A may offeroptions to the user through the user interface 4218 for modifying thedetermined protocol. For example, if it is determined that the biosensor4212 is to be used for amplification, the sequencing system 4200A mayrequest a temperature for the annealing cycle. Furthermore, thesequencing system 4200A may issue warnings to a user if a user hasprovided user inputs that are generally not acceptable for the selectedassay protocol.

In implementations, the biosensor 4212 includes millions of sensors (orpixels), each of which generates a plurality of sequences of pixelsignals over successive base calling cycles. The analysis module 4244detects the plurality of sequences of pixel signals and attributes themto corresponding sensors (or pixels) in accordance to the row-wiseand/or column-wise location of the sensors on an array of sensors.

FIG. 42C is a simplified block diagram of a system for analysis ofsensor data from the sequencing system 4200A, such as base call sensoroutputs. In the example of FIG. 42C, the system includes theconfigurable processor 4246. The configurable processor 4246 can executea base caller (e.g., the neural network-based base caller 2900) incoordination with a runtime program/logic 4280 executed by the centralprocessing unit (CPU) 4252 (i.e., a host processor). The sequencingsystem 4200A comprises the biosensor 4212 and flow cells. The flow cellscan comprise one or more tiles in which clusters of genetic material areexposed to a sequence of cluster flows used to cause reactions in theclusters to identify the bases in the genetic material. The sensorssense the reactions for each cycle of the sequence in each tile of theflow cell to provide tile data. Genetic sequencing is a data intensiveoperation, which translates base call sensor data into sequences of basecalls for each cluster of genetic material sensed in during a base calloperation.

The system in this example includes the CPU 4252, which executes aruntime program/logic 4280 to coordinate the base call operations,memory 4248B to store sequences of arrays of tile data, base call readsproduced by the base calling operation, and other information used inthe base call operations. Also, in this illustration the system includesmemory 4248A to store a configuration file (or files), such as FPGA bitfiles, and model parameters for the neural networks used to configureand reconfigure the configurable processor 4246, and execute the neuralnetworks. The sequencing system 4200A can include a program forconfiguring a configurable processor and in some implementations areconfigurable processor to execute the neural networks.

The sequencing system 4200A is coupled by a bus 4289 to the configurableprocessor 4246. The bus 4289 can be implemented using a high throughputtechnology, such as in one example bus technology compatible with thePCIe standards (Peripheral Component Interconnect Express) currentlymaintained and developed by the PCI-SIG (PCI Special Interest Group).Also in this example, a memory 4248A is coupled to the configurableprocessor 4246 by bus 4293. The memory 4248A can be on-board memory,disposed on a circuit board with the configurable processor 4246. Thememory 4248A is used for high speed access by the configurable processor4246 of working data used in the base call operation. The bus 4293 canalso be implemented using a high throughput technology, such as bustechnology compatible with the PCIe standards.

Configurable processors, including field programmable gate arrays FPGAs,coarse grained reconfigurable arrays CGRAs, and other configurable andreconfigurable devices, can be configured to implement a variety offunctions more efficiently or faster than might be achieved using ageneral purpose processor executing a computer program. Configuration ofconfigurable processors involves compiling a functional description toproduce a configuration file, referred to sometimes as a bitstream orbit file, and distributing the configuration file to the configurableelements on the processor. The configuration file defines the logicfunctions to be executed by the configurable processor, by configuringthe circuit to set data flow patterns, use of distributed memory andother on-chip memory resources, lookup table contents, operations ofconfigurable logic blocks and configurable execution units likemultiply-and-accumulate units, configurable interconnects and otherelements of the configurable array. A configurable processor isreconfigurable if the configuration file may be changed in the field, bychanging the loaded configuration file. For example, the configurationfile may be stored in volatile SRAM elements, in non-volatile read-writememory elements, and in combinations of the same, distributed among thearray of configurable elements on the configurable or reconfigurableprocessor. A variety of commercially available configurable processorsare suitable for use in a base calling operation as described herein.Examples include Google's Tensor Processing Unit (TPU)™, rackmountsolutions like GX4 Rackmount Series™, GX9 Rackmount Series™, NVIDIADGX-1™, Microsoft' Stratix V FPGA™, Graphcore's Intelligent ProcessorUnit (IPU)™, Qualcomm's Zeroth Platform™ with Snapdragon Processors™,NVIDIA's Volta™, NVIDIA's DRIVE PX™, NVIDIA's JETSON TX1/TX2 MODULE™,Intel's Nirvana™, Movidius VPU™, Fujitsu DPI™, ARM's DynamicIQ™, IBMTrueNorth™, Lambda GPU Server with Testa V100s™, Xilinx Alveo™ U200,Xilinx Alveo™ U2190, Xilinx Alveo™ U280, Intel/Altera Stratix™ GX2800,Intel/Altera Stratix™ GX2800, and Intel Stratix™ GX10M. In someexamples, a host CPU can be implemented on the same integrated circuitas the configurable processor.

Implementations described herein implement the neural network-based basecaller 2900 using the configurable processor 4246. The configurationfile for the configurable processor 4246 can be implemented byspecifying the logic functions to be executed using a high leveldescription language HDL or a register transfer level RTL languagespecification. The specification can be compiled using the resourcesdesigned for the selected configurable processor to generate theconfiguration file. The same or similar specification can be compiledfor the purposes of generating a design for an application-specificintegrated circuit which may not be a configurable processor.

Alternatives for the configurable processor configurable processor 4246,in all implementations described herein, therefore include a configuredprocessor comprising an application specific ASIC or special purposeintegrated circuit or set of integrated circuits, or a system-on-a-chipSOC device, or a graphics processing unit (GPU) processor or acoarse-grained reconfigurable architecture (CGRA) processor, configuredto execute a neural network based base call operation as describedherein.

In general, configurable processors and configured processors describedherein, as configured to execute runs of a neural network, are referredto herein as neural network processors.

The configurable processor 4246 is configured in this example by aconfiguration file loaded using a program executed by the CPU 4252, orby other sources, which configures the array of configurable elements4291 (e.g., configuration logic blocks (CLB) such as look up tables(LUTs), flip-flops, compute processing units (PMUs), and compute memoryunits (CMUs), configurable I/O blocks, programmable interconnects), onthe configurable processor to execute the base call function. In thisexample, the configuration includes data flow logic 4297 which iscoupled to the buses 4289 and 4293 and executes functions fordistributing data and control parameters among the elements used in thebase call operation.

Also, the configurable processor 4246 is configured with data flow logic4297 to execute the neural network-based base caller 2900. The logic4297 comprises multi-cycle execution clusters (e.g., 4279) which, inthis example, includes execution cluster 1 through execution cluster X.The number of multi-cycle execution clusters can be selected accordingto a trade-off involving the desired throughput of the operation, andthe available resources on the configurable processor 4246.

The multi-cycle execution clusters are coupled to the data flow logic4297 by data flow paths 4299 implemented using configurable interconnectand memory resources on the configurable processor 4246. Also, themulti-cycle execution clusters are coupled to the data flow logic 4297by control paths 4295 implemented using configurable interconnect andmemory resources for example on the configurable processor 4246, whichprovide control signals indicating available execution clusters,readiness to provide input units for execution of a run of the neuralnetwork-based base caller 2900 to the available execution clusters,readiness to provide trained parameters for the neural network-basedbase caller 2900, readiness to provide output patches of base callclassification data, and other control data used for execution of theneural network-based base caller 2900.

The configurable processor 4246 is configured to execute runs of theneural network-based base caller 2900 using trained parameters toproduce classification data for the sensing cycles of the base callingoperation. A run of the neural network-based base caller 2900 isexecuted to produce classification data for a subject sensing cycle ofthe base calling operation. A run of the neural network-based basecaller 2900 operates on a sequence including a number N of arrays oftile data from respective sensing cycles of N sensing cycles, where theN sensing cycles provide sensor data for different base call operationsfor one base position per operation in time sequence in the examplesdescribed herein. Optionally, some of the N sensing cycles can be out ofsequence if needed according to a particular neural network model beingexecuted. The number N can be any number greater than one. In someexamples described herein, sensing cycles of the N sensing cyclesrepresent a set of sensing cycles for at least one sensing cyclepreceding the subject sensing cycle and at least one sensing cyclefollowing the subject cycle in time sequence. Examples are describedherein in which the number N is an integer equal to or greater thanfive.

The data flow logic 4297 is configured to move tile data and at leastsome trained parameters of the model parameters from the memory 4248A tothe configurable processor 4246 for runs of the neural network-basedbase caller 2900, using input units for a given run including tile datafor spatially aligned patches of the N arrays. The input units can bemoved by direct memory access operations in one DMA operation, or insmaller units moved during available time slots in coordination with theexecution of the neural network deployed.

Tile data for a sensing cycle as described herein can comprise an arrayof sensor data having one or more features. For example, the sensor datacan comprise two images which are analyzed to identify one of four basesat a base position in a genetic sequence of DNA, RNA, or other geneticmaterial. The tile data can also include metadata about the images andthe sensors. For example, in implementations of the base callingoperation, the tile data can comprise information about alignment of theimages with the clusters such as distance from center informationindicating the distance of each pixel in the array of sensor data fromthe center of a cluster of genetic material on the tile.

During execution of the neural network-based base caller 2900 asdescribed below, tile data can also include data produced duringexecution of the neural network-based base caller 2900, referred to asintermediate data, which can be reused rather than recomputed during arun of the neural network-based base caller 2900. For example, duringexecution of the neural network-based base caller 2900, the data flowlogic 4297 can write intermediate data to the memory 4248A in place ofthe sensor data for a given patch of an array of tile data.Implementations like this are described in more detail below.

As illustrated, a system is described for analysis of base call sensoroutput, comprising memory (e.g., 4248A) accessible by the runtimeprogram/logic 4280 storing tile data including sensor data for a tilefrom sensing cycles of a base calling operation. Also, the systemincludes a neural network processor, such as configurable processor 4246having access to the memory. The neural network processor is configuredto execute runs of a neural network using trained parameters to produceclassification data for sensing cycles. As described herein, a run ofthe neural network is operating on a sequence of N arrays of tile datafrom respective sensing cycles of N sensing cycles, including a subjectcycle, to produce the classification data for the subject cycle. Thedata flow logic 4297 is provided to move tile data and the trainedparameters from the memory to the neural network processor for runs ofthe neural network using input units including data for spatiallyaligned patches of the N arrays from respective sensing cycles of Nsensing cycles.

Also, a system is described in which the neural network processor hasaccess to the memory, and includes a plurality of execution clusters,the execution clusters in the plurality of execution clusters configuredto execute a neural network. The data flow logic 1997 has access to thememory and to execution clusters in the plurality of execution clusters,to provide input units of tile data to available execution clusters inthe plurality of execution clusters, the input units including a numberN of spatially aligned patches of arrays of tile data from respectivesensing cycles, including a subject sensing cycle, and to cause theexecution clusters to apply the N spatially aligned patches to theneural network to produce output patches of classification data for thespatially aligned patch of the subject sensing cycle, where N is greaterthan 1.

FIG. 43A is a simplified diagram showing aspects of the base callingoperation, including functions of a runtime program (e.g., the runtimelogic 4280) executed by a host processor. In this diagram, the output ofimage sensors from a flow cell are provided on lines 4300 to imageprocessing threads 4301, which can perform processes on images such asalignment and arrangement in an array of sensor data for the individualtiles and resampling of images, and can be used by processes whichcalculate a tile cluster mask for each tile in the flow cell, whichidentifies pixels in the array of sensor data that correspond toclusters of genetic material on the corresponding tile of the flow cell.The outputs of the image processing threads 4301 are provided on lines4302 to a dispatch logic 4303 in the CPU which routes the arrays of tiledata to a data cache 4305 (e.g., SSD storage) on a high-speed bus 4304,or on high-speed bus 4306 to the neural network processor hardware 4307,such as the configurable processor 1946 of FIG. 19C, according to thestate of the base calling operation. The processed and transformedimages can be stored on the data cache 4305 for sensing cycles that werepreviously used. The hardware 4307 returns classification data output bythe neural network to the dispatch logic 4303, which passes theinformation to the data cache 4305, or on lines 4308 to threads 4309that perform base call and quality score computations using theclassification data, and can arrange the data in standard formats forbase call reads. The outputs of the threads 4309 that perform basecalling and quality score computations are provided on lines 4310 tothreads 4311 that aggregate the base call reads, perform otheroperations such as data compression, and write the resulting base calloutputs to specified destinations for utilization by the customers.

In some implementations, the host can include threads (not shown) thatperform final processing of the output of the hardware 4307 in supportof the neural network. For example, the hardware 4307 can provideoutputs of classification data from a final layer of the multi-clusterneural network. The host processor can execute an output activationfunction, such as a softmax function, over the classification data toconfigure the data for use by the base call and quality score threads4302. Also, the host processor can execute input operations (not shown),such as batch normalization of the tile data prior to input to thehardware 4307.

FIG. 43B is a simplified diagram of a configuration of a configurableprocessor 1946 such as that of FIG. 19C. In FIG. 43B, the configurableprocessor 1946 comprises an FPGA with a plurality of high speed PCIeinterfaces. The FPGA is configured with a wrapper 4390 which comprisesthe data flow logic 1997 described with reference to FIG. 19C. Thewrapper 4390 manages the interface and coordination with a runtimeprogram in the CPU across the CPU communication link 4377 and managescommunication with the on-board DRAM 4399 (e.g., memory 1448A) via DRAMcommunication link 4397. The data flow logic 1997 in the wrapper 4390provides patch data retrieved by traversing the arrays of tile data onthe on-board DRAM 4399 for the number N cycles to a cluster 4385, andretrieves process data 4387 from the cluster 4385 for delivery back tothe on-board DRAM 4399. The wrapper 4390 also manages transfer of databetween the on-board DRAM 4399 and host memory, for both the inputarrays of tile data, and for the output patches of classification data.The wrapper transfers patch data on line 4383 to the allocated cluster4385. The wrapper provides trained parameters, such as weights andbiases on line 4381 to the cluster 4385 retrieved from the on-board DRAM4399. The wrapper provides configuration and control data on line 4379to the cluster 4385 provided from, or generated in response to, theruntime program on the host via the CPU communication link 4377. Thecluster can also provide status signals on line 4389 to the wrapper4390, which are used in cooperation with control signals from the hostto manage traversal of the arrays of tile data to provide spatiallyaligned patch data, and to execute the multi-cycle neural network overthe patch data using the resources of the cluster 4385.

As mentioned above, there can be multiple clusters on a singleconfigurable processor managed by the wrapper 4390 configured forexecuting on corresponding ones of multiple patches of the tile data.Each cluster can be configured to provide classification data for basecalls in a subject sensing cycle using the tile data of multiple sensingcycles described herein.

In examples of the system, model data, including kernel data like filterweights and biases can be sent from the host CPU to the configurableprocessor, so that the model can be updated as a function of cyclenumber. A base calling operation can comprise, for a representativeexample, on the order of hundreds of sensing cycles. Base callingoperation can include paired end reads in some implementations. Forexample, the model trained parameters may be updated once every 20cycles (or other number of cycles), or according to update patternsimplemented for particular systems and neural network models. In someimplementations including paired end reads in which a sequence for agiven string in a genetic cluster on a tile includes a first partextending from a first end down (or up) the string, and a second partextending from a second end up (or down) the string, the trainedparameters can be updated on the transition from the first part to thesecond part.

In some examples, image data for multiple cycles of sensing data for atile can be sent from the CPU to the wrapper 4390. The wrapper 4390 canoptionally do some pre-processing and transformation of the sensing dataand write the information to the on-board DRAM 4399. The input tile datafor each sensing cycle can include arrays of sensor data including onthe order of 4000×3000 pixels per sensing cycle per tile or more, withtwo features representing colors of two images of the tile, and one ortwo bytes per feature per pixel. For an implementation in which thenumber N is three sensing cycles to be used in each run of themulti-cycle neural network, the array of tile data for each run of themulti-cycle neural network can consume on the order of hundreds ofmegabytes per tile. In some implementations of the system, the tile dataalso includes an array of distance-from-cluster center (DFC) data,stored once per tile, or other type of metadata about the sensor dataand the tiles.

In operation, when a multi-cycle cluster is available, the wrapperallocates a patch to the cluster. The wrapper fetches a next patch oftile data in the traversal of the tile and sends it to the allocatedcluster along with appropriate control and configuration information.The cluster can be configured with enough memory on the configurableprocessor to hold a patch of data including patches from multiple cyclesin some systems, that is being worked on in place, and a patch of datathat is to be worked on when the current patch of processing is finishedusing a ping-pong buffer technique or raster scanning technique invarious implementations.

When an allocated cluster completes its run of the neural network forthe current patch and produces an output patch, it will signal thewrapper. The wrapper will read the output patch from the allocatedcluster, or alternatively the allocated cluster will push the data outto the wrapper. Then the wrapper will assemble output patches for theprocessed tile in the DRAM 4399. When the processing of the entire tilehas been completed, and the output patches of data transferred to theDRAM, the wrapper sends the processed output array for the tile back tothe host/CPU in a specified format. In some implementations, theon-board DRAM 4399 is managed by memory management logic in the wrapper4390. The runtime program can control the sequencing operations tocomplete analysis of all the arrays of tile data for all the cycles inthe run in a continuous flow to provide real time analysis.

FIG. 44 illustrates one implementation of determining state data on aCPU, and loading the state data from the CPU to an FPGA for basecalling.

Terminology and Additional Implementations

Base calling includes incorporation or attachment of afluorescently-labeled tag with an analyte. The analyte can be anucleotide or an oligonucleotide, and the tag can be for a particularnucleotide type (A, C, T, or G). Excitation light is directed toward theanalyte having the tag, and the tag emits a detectable fluorescentsignal or intensity emission. The intensity emission is indicative ofphotons emitted by the excited tag that is chemically attached to theanalyte.

Throughout this application, including the claims, when phrases such asor similar to “images, image data, or image regions depicting intensityemissions of analytes and their surrounding background” are used, theyrefer to the intensity emissions of the tags attached to the analytes. Aperson skilled in the art will appreciate that the intensity emissionsof the attached tags are representative of or equivalent to theintensity emissions of the analytes to which the tags are attached, andare therefore used interchangeably. Similarly, properties of theanalytes refer to properties of the tags attached to the analytes or ofthe intensity emissions from the attached tags. For example, a center ofan analyte refers to the center of the intensity emissions emitted by atag attached to the analyte. In another example, the surroundingbackground of an analyte refers to the surrounding background of theintensity emissions emitted by a tag attached to the analyte.

All literature and similar material cited in this application,including, but not limited to, patents, patent applications, articles,books, treatises, and web pages, regardless of the format of suchliterature and similar materials, are expressly incorporated byreference in their entirety. In the event that one or more of theincorporated literature and similar materials differs from orcontradicts this application, including but not limited to definedterms, term usage, described techniques, or the like, this applicationcontrols.

The technology disclosed uses neural networks to improve the quality andquantity of nucleic acid sequence information that can be obtained froma nucleic acid sample such as a nucleic acid template or its complement,for instance, a DNA or RNA polynucleotide or other nucleic acid sample.Accordingly, certain implementations of the technology disclosed providehigher throughput polynucleotide sequencing, for instance, higher ratesof collection of DNA or RNA sequence data, greater efficiency insequence data collection, and/or lower costs of obtaining such sequencedata, relative to previously available methodologies.

The technology disclosed uses neural networks to identify the center ofa solid-phase nucleic acid cluster and to analyze optical signals thatare generated during sequencing of such clusters, to discriminateunambiguously between adjacent, abutting or overlapping clusters inorder to assign a sequencing signal to a single, discrete sourcecluster. These and related implementations thus permit retrieval ofmeaningful information, such as sequence data, from regions ofhigh-density cluster arrays where useful information could notpreviously be obtained from such regions due to confounding effects ofoverlapping or very closely spaced adjacent clusters, including theeffects of overlapping signals (e.g., as used in nucleic acidsequencing) emanating therefrom.

As described in greater detail below, in certain implementations thereis provided a composition that comprises a solid support havingimmobilized thereto one or a plurality of nucleic acid clusters asprovided herein. Each cluster comprises a plurality of immobilizednucleic acids of the same sequence and has an identifiable center havinga detectable center label as provided herein, by which the identifiablecenter is distinguishable from immobilized nucleic acids in asurrounding region in the cluster. Also described herein are methods formaking and using such clusters that have identifiable centers.

The presently disclosed implementations will find uses in numeroussituations where advantages are obtained from the ability to identify,determine, annotate, record or otherwise assign the position of asubstantially central location within a cluster, such as high-throughputnucleic acid sequencing, development of image analysis algorithms forassigning optical or other signals to discrete source clusters, andother applications where recognition of the center of an immobilizednucleic acid cluster is desirable and beneficial.

In certain implementations, the present invention contemplates methodsthat relate to high-throughput nucleic acid analysis such as nucleicacid sequence determination (e.g., “sequencing”). Exemplaryhigh-throughput nucleic acid analyses include without limitation de novosequencing, re-sequencing, whole genome sequencing, gene expressionanalysis, gene expression monitoring, epigenetic analysis, genomemethylation analysis, allele specific primer extension (APSE), geneticdiversity profiling, whole genome polymorphism discovery and analysis,single nucleotide polymorphism analysis, hybridization based sequencedetermination methods, and the like. One skilled in the art willappreciate that a variety of different nucleic acids can be analyzedusing the methods and compositions of the present invention.

Although the implementations of the present invention are described inrelation to nucleic acid sequencing, they are applicable in any fieldwhere image data acquired at different time points, spatial locations orother temporal or physical perspectives is analyzed. For example, themethods and systems described herein are useful in the fields ofmolecular and cell biology where image data from microarrays, biologicalspecimens, cells, organisms and the like is acquired and at differenttime points or perspectives and analyzed. Images can be obtained usingany number of techniques known in the art including, but not limited to,fluorescence microscopy, light microscopy, confocal microscopy, opticalimaging, magnetic resonance imaging, tomography scanning or the like. Asanother example, the methods and systems described herein can be appliedwhere image data obtained by surveillance, aerial or satellite imagingtechnologies and the like is acquired at different time points orperspectives and analyzed. The methods and systems are particularlyuseful for analyzing images obtained for a field of view in which theanalytes being viewed remain in the same locations relative to eachother in the field of view. The analytes may however havecharacteristics that differ in separate images, for example, theanalytes may appear different in separate images of the field of view.For example, the analytes may appear different with regard to the colorof a given analyte detected in different images, a change in theintensity of signal detected for a given analyte in different images, oreven the appearance of a signal for a given analyte in one image anddisappearance of the signal for the analyte in another image.

Examples described herein may be used in various biological or chemicalprocesses and systems for academic or commercial analysis. Morespecifically, examples described herein may be used in various processesand systems where it is desired to detect an event, property, quality,or characteristic that is indicative of a designated reaction. Forexample, examples described herein include light detection devices,biosensors, and their components, as well as bioassay systems thatoperate with biosensors. In some examples, the devices, biosensors andsystems may include a flow cell and one or more light sensors that arecoupled together (removably or fixedly) in a substantially unitarystructure.

The devices, biosensors and bioassay systems may be configured toperform a plurality of designated reactions that may be detectedindividually or collectively. The devices, biosensors and bioassaysystems may be configured to perform numerous cycles in which theplurality of designated reactions occurs in parallel. For example, thedevices, biosensors and bioassay systems may be used to sequence a densearray of DNA features through iterative cycles of enzymatic manipulationand light or image detection/acquisition. As such, the devices,biosensors and bioassay systems (e.g., via one or more cartridges) mayinclude one or more microfluidic channel that delivers reagents or otherreaction components in a reaction solution to a reaction site of thedevices, biosensors and bioassay systems. In some examples, the reactionsolution may be substantially acidic, such as comprising a pH of lessthan or equal to about 5, or less than or equal to about 4, or less thanor equal to about 3. In some other examples, the reaction solution maybe substantially alkaline/basic, such as comprising a pH of greater thanor equal to about 8, or greater than or equal to about 9, or greaterthan or equal to about 10. As used herein, the term “acidity” andgrammatical variants thereof refer to a pH value of less than about 7,and the terms “basicity,” “alkalinity” and grammatical variants thereofrefer to a pH value of greater than about 7.

In some examples, the reaction sites are provided or spaced apart in apredetermined manner, such as in a uniform or repeating pattern. In someother examples, the reaction sites are randomly distributed. Each of thereaction sites may be associated with one or more light guides and oneor more light sensors that detect light from the associated reactionsite. In some examples, the reaction sites are located in reactionrecesses or chambers, which may at least partially compartmentalize thedesignated reactions therein.

As used herein, a “designated reaction” includes a change in at leastone of a chemical, electrical, physical, or optical property (orquality) of a chemical or biological substance of interest, such as ananalyte-of-interest. In particular examples, a designated reaction is apositive binding event, such as incorporation of a fluorescently labeledbiomolecule with an analyte-of-interest, for example. More generally, adesignated reaction may be a chemical transformation, chemical change,or chemical interaction. A designated reaction may also be a change inelectrical properties. In particular examples, a designated reactionincludes the incorporation of a fluorescently-labeled molecule with ananalyte. The analyte may be an oligonucleotide and thefluorescently-labeled molecule may be a nucleotide. A designatedreaction may be detected when an excitation light is directed toward theoligonucleotide having the labeled nucleotide, and the fluorophore emitsa detectable fluorescent signal. In alternative examples, the detectedfluorescence is a result of chemiluminescence or bioluminescence. Adesignated reaction may also increase fluorescence (or Förster)resonance energy transfer (FRET), for example, by bringing a donorfluorophore in proximity to an acceptor fluorophore, decrease FRET byseparating donor and acceptor fluorophores, increase fluorescence byseparating a quencher from a fluorophore, or decrease fluorescence byco-locating a quencher and fluorophore.

As used herein, a “reaction solution,” “reaction component” or“reactant” includes any substance that may be used to obtain at leastone designated reaction. For example, potential reaction componentsinclude reagents, enzymes, samples, other biomolecules, and buffersolutions, for example. The reaction components may be delivered to areaction site in a solution and/or immobilized at a reaction site. Thereaction components may interact directly or indirectly with anothersubstance, such as an analyte-of-interest immobilized at a reactionsite. As noted above, the reaction solution may be substantially acidic(i.e., include a relatively high acidity) (e.g., comprising a pH of lessthan or equal to about 5, a pH less than or equal to about 4, or a pHless than or equal to about 3) or substantially alkaline/basic (i.e.,include a relatively high alkalinity/basicity) (e.g., comprising a pH ofgreater than or equal to about 8, a pH of greater than or equal to about9, or a pH of greater than or equal to about 10).

As used herein, the term “reaction site” is a localized region where atleast one designated reaction may occur. A reaction site may includesupport surfaces of a reaction structure or substrate where a substancemay be immobilized thereon. For example, a reaction site may include asurface of a reaction structure (which may be positioned in a channel ofa flow cell) that has a reaction component thereon, such as a colony ofnucleic acids thereon. In some such examples, the nucleic acids in thecolony have the same sequence, being for example, clonal copies of asingle stranded or double stranded template. However, in some examples areaction site may contain only a single nucleic acid molecule, forexample, in a single stranded or double stranded form.

A plurality of reaction sites may be randomly distributed along thereaction structure or arranged in a predetermined manner (e.g.,side-by-side in a matrix, such as in microarrays). A reaction site canalso include a reaction chamber or recess that at least partiallydefines a spatial region or volume configured to compartmentalize thedesignated reaction. As used herein, the term “reaction chamber” or“reaction recess” includes a defined spatial region of the supportstructure (which is often in fluid communication with a flow channel). Areaction recess may be at least partially separated from the surroundingenvironment other or spatial regions. For example, a plurality ofreaction recesses may be separated from each other by shared walls, suchas a detection surface. As a more specific example, the reactionrecesses may be nanowells comprising an indent, pit, well, groove,cavity or depression defined by interior surfaces of a detection surfaceand have an opening or aperture (i.e., be open-sided) so that thenanowells can be in fluid communication with a flow channel.

In some examples, the reaction recesses of the reaction structure aresized and shaped relative to solids (including semi-solids) so that thesolids may be inserted, fully or partially, therein. For example, thereaction recesses may be sized and shaped to accommodate a capture bead.The capture bead may have clonally amplified DNA or other substancesthereon. Alternatively, the reaction recesses may be sized and shaped toreceive an approximate number of beads or solid substrates. As anotherexample, the reaction recesses may be filled with a porous gel orsubstance that is configured to control diffusion or filter fluids orsolutions that may flow into the reaction recesses.

In some examples, light sensors (e.g., photodiodes) are associated withcorresponding reaction sites. A light sensor that is associated with areaction site is configured to detect light emissions from theassociated reaction site via at least one light guide when a designatedreaction has occurred at the associated reaction site. In some cases, aplurality of light sensors (e.g. several pixels of a light detection orcamera device) may be associated with a single reaction site. In othercases, a single light sensor (e.g. a single pixel) may be associatedwith a single reaction site or with a group of reaction sites. The lightsensor, the reaction site, and other features of the biosensor may beconfigured so that at least some of the light is directly detected bythe light sensor without being reflected.

As used herein, a “biological or chemical substance” includesbiomolecules, samples-of-interest, analytes-of-interest, and otherchemical compound(s). A biological or chemical substance may be used todetect, identify, or analyze other chemical compound(s), or function asintermediaries to study or analyze other chemical compound(s). Inparticular examples, the biological or chemical substances include abiomolecule. As used herein, a “biomolecule” includes at least one of abiopolymer, nucleoside, nucleic acid, polynucleotide, oligonucleotide,protein, enzyme, polypeptide, antibody, antigen, ligand, receptor,polysaccharide, carbohydrate, polyphosphate, cell, tissue, organism, orfragment thereof or any other biologically active chemical compound(s)such as analogs or mimetics of the aforementioned species. In a furtherexample, a biological or chemical substance or a biomolecule includes anenzyme or reagent used in a coupled reaction to detect the product ofanother reaction such as an enzyme or reagent, such as an enzyme orreagent used to detect pyrophosphate in a pyrosequencing reaction.Enzymes and reagents useful for pyrophosphate detection are described,for example, in U.S. Patent Publication No. 2005/0244870 A1, which isincorporated by reference in its entirety.

Biomolecules, samples, and biological or chemical substances may benaturally occurring or synthetic and may be suspended in a solution ormixture within a reaction recess or region. Biomolecules, samples, andbiological or chemical substances may also be bound to a solid phase orgel material. Biomolecules, samples, and biological or chemicalsubstances may also include a pharmaceutical composition. In some cases,biomolecules, samples, and biological or chemical substances of interestmay be referred to as targets, probes, or analytes.

As used herein, a “biosensor” includes a device that includes a reactionstructure with a plurality of reaction sites that is configured todetect designated reactions that occur at or proximate to the reactionsites. A biosensor may include a solid-state light detection or“imaging” device (e.g., CCD or CMOS light detection device) and,optionally, a flow cell mounted thereto. The flow cell may include atleast one flow channel that is in fluid communication with the reactionsites. As one specific example, the biosensor is configured tofluidically and electrically couple to a bioassay system. The bioassaysystem may deliver a reaction solution to the reaction sites accordingto a predetermined protocol (e.g., sequencing-by-synthesis) and performa plurality of imaging events. For example, the bioassay system maydirect reaction solutions to flow along the reaction sites. At least oneof the reaction solutions may include four types of nucleotides havingthe same or different fluorescent labels. The nucleotides may bind tothe reaction sites, such as to corresponding oligonucleotides at thereaction sites. The bioassay system may then illuminate the reactionsites using an excitation light source (e.g., solid-state light sources,such as light-emitting diodes (LEDs)). The excitation light may have apredetermined wavelength or wavelengths, including a range ofwavelengths. The fluorescent labels excited by the incident excitationlight may provide emission signals (e.g., light of a wavelength orwavelengths that differ from the excitation light and, potentially, eachother) that may be detected by the light sensors.

As used herein, the term “immobilized,” when used with respect to abiomolecule or biological or chemical substance, includes substantiallyattaching the biomolecule or biological or chemical substance at amolecular level to a surface, such as to a detection surface of a lightdetection device or reaction structure. For example, a biomolecule orbiological or chemical substance may be immobilized to a surface of thereaction structure using adsorption techniques including non-covalentinteractions (e.g., electrostatic forces, van der Waals, and dehydrationof hydrophobic interfaces) and covalent binding techniques wherefunctional groups or linkers facilitate attaching the biomolecules tothe surface. Immobilizing biomolecules or biological or chemicalsubstances to the surface may be based upon the properties of thesurface, the liquid medium carrying the biomolecule or biological orchemical substance, and the properties of the biomolecules or biologicalor chemical substances themselves. In some cases, the surface may befunctionalized (e.g., chemically or physically modified) to facilitateimmobilizing the biomolecules (or biological or chemical substances) tothe surface.

In some examples, nucleic acids can be immobilized to the reactionstructure, such as to surfaces of reaction recesses thereof. Inparticular examples, the devices, biosensors, bioassay systems andmethods described herein may include the use of natural nucleotides andalso enzymes that are configured to interact with the naturalnucleotides. Natural nucleotides include, for example, ribonucleotidesor deoxyribonucleotides. Natural nucleotides can be in the mono-, di-,or tri-phosphate form and can have a base selected from adenine (A),Thymine (T), uracil (U), guanine (G) or cytosine (C). It will beunderstood, however, that non-natural nucleotides, modified nucleotidesor analogs of the aforementioned nucleotides can be used.

As noted above, a biomolecule or biological or chemical substance may beimmobilized at a reaction site in a reaction recess of a reactionstructure. Such a biomolecule or biological substance may be physicallyheld or immobilized within the reaction recesses through an interferencefit, adhesion, covalent bond, or entrapment. Examples of items or solidsthat may be disposed within the reaction recesses include polymer beads,pellets, agarose gel, powders, quantum dots, or other solids that may becompressed and/or held within the reaction chamber. In certainimplementations, the reaction recesses may be coated or filled with ahydrogel layer capable of covalently binding DNA oligonucleotides. Inparticular examples, a nucleic acid superstructure, such as a DNA ball,can be disposed in or at a reaction recess, for example, by attachmentto an interior surface of the reaction recess or by residence in aliquid within the reaction recess. A DNA ball or other nucleic acidsuperstructure can be performed and then disposed in or at a reactionrecess. Alternatively, a DNA ball can be synthesized in situ at areaction recess. A substance that is immobilized in a reaction recesscan be in a solid, liquid, or gaseous state.

As used herein, the term “analyte” is intended to mean a point or areain a pattern that can be distinguished from other points or areasaccording to relative location. An individual analyte can include one ormore molecules of a particular type. For example, an analyte can includea single target nucleic acid molecule having a particular sequence or ananalyte can include several nucleic acid molecules having the samesequence (and/or complementary sequence, thereof). Different moleculesthat are at different analytes of a pattern can be differentiated fromeach other according to the locations of the analytes in the pattern.Example analytes include without limitation, wells in a substrate, beads(or other particles) in or on a substrate, projections from a substrate,ridges on a substrate, pads of gel material on a substrate, or channelsin a substrate.

Any of a variety of target analytes that are to be detected,characterized, or identified can be used in an apparatus, system ormethod set forth herein. Exemplary analytes include, but are not limitedto, nucleic acids (e.g., DNA, RNA or analogs thereof), proteins,polysaccharides, cells, antibodies, epitopes, receptors, ligands,enzymes (e.g. kinases, phosphatases or polymerases), small molecule drugcandidates, cells, viruses, organisms, or the like.

The terms “analyte”, “nucleic acid”, “nucleic acid molecule”, and“polynucleotide” are used interchangeably herein. In variousimplementations, nucleic acids may be used as templates as providedherein (e.g., a nucleic acid template, or a nucleic acid complement thatis complementary to a nucleic acid nucleic acid template) for particulartypes of nucleic acid analysis, including but not limited to nucleicacid amplification, nucleic acid expression analysis, and/or nucleicacid sequence determination or suitable combinations thereof. Nucleicacids in certain implementations include, for instance, linear polymersof deoxyribonucleotides in 3′-5′ phosphodiester or other linkages, suchas deoxyribonucleic acids (DNA), for example, single- anddouble-stranded DNA, genomic DNA, copy DNA or complementary DNA (cDNA),recombinant DNA, or any form of synthetic or modified DNA. In otherimplementations, nucleic acids include for instance, linear polymers ofribonucleotides in 3′-5′ phosphodiester or other linkages such asribonucleic acids (RNA), for example, single- and double-stranded RNA,messenger (mRNA), copy RNA or complementary RNA (cRNA), alternativelyspliced mRNA, ribosomal RNA, small nucleolar RNA (snoRNA), microRNAs(miRNA), small interfering RNAs (sRNA), piwi RNAs (piRNA), or any formof synthetic or modified RNA. Nucleic acids used in the compositions andmethods of the present invention may vary in length and may be intact orfull-length molecules or fragments or smaller parts of larger nucleicacid molecules. In particular implementations, a nucleic acid may haveone or more detectable labels, as described elsewhere herein.

The terms “analyte”, “cluster”, “nucleic acid cluster”, “nucleic acidcolony”, and “DNA cluster” are used interchangeably and refer to aplurality of copies of a nucleic acid template and/or complementsthereof attached to a solid support. Typically and in certain preferredimplementations, the nucleic acid cluster comprises a plurality ofcopies of template nucleic acid and/or complements thereof, attached viatheir 5′ termini to the solid support. The copies of nucleic acidstrands making up the nucleic acid clusters may be in a single or doublestranded form. Copies of a nucleic acid template that are present in acluster can have nucleotides at corresponding positions that differ fromeach other, for example, due to presence of a label moiety. Thecorresponding positions can also contain analog structures havingdifferent chemical structure but similar Watson-Crick base-pairingproperties, such as is the case for uracil and thymine.

Colonies of nucleic acids can also be referred to as “nucleic acidclusters”. Nucleic acid colonies can optionally be created by clusteramplification or bridge amplification techniques as set forth in furtherdetail elsewhere herein. Multiple repeats of a target sequence can bepresent in a single nucleic acid molecule, such as a concatamer createdusing a rolling circle amplification procedure.

The nucleic acid clusters of the invention can have different shapes,sizes and densities depending on the conditions used. For example,clusters can have a shape that is substantially round, multi-sided,donut-shaped or ring-shaped. The diameter of a nucleic acid cluster canbe designed to be from about 0.2 μm to about 6 μm, about 0.3 μm to about4 μm, about 0.4 μm to about 3 μm, about 0.5 μm to about 2 μm, about 0.75μm to about 1.5 μm, or any intervening diameter. In a particularimplementation, the diameter of a nucleic acid cluster is about 0.5 μm,about 1 μm, about 1.5 μm, about 2 μm, about 2.5 μm, about 3 μm, about 4m, about 5 μm, or about 6 μm. The diameter of a nucleic acid cluster maybe influenced by a number of parameters, including, but not limited tothe number of amplification cycles performed in producing the cluster,the length of the nucleic acid template or the density of primersattached to the surface upon which clusters are formed. The density ofnucleic acid clusters can be designed to typically be in the range of0.1/mm², 1/mm², 10/mm², 100/mm², 1,000/mm², 10,000/mm² to 100,000/mm².The present invention further contemplates, in part, higher densitynucleic acid clusters, for example, 100,000/mm² to 1,000,000/mm² and1,000,000/mm² to 10,000,000/mm².

As used herein, an “analyte” is an area of interest within a specimen orfield of view. When used in connection with microarray devices or othermolecular analytical devices, an analyte refers to the area occupied bysimilar or identical molecules. For example, an analyte can be anamplified oligonucleotide or any other group of a polynucleotide orpolypeptide with a same or similar sequence. In other implementations,an analyte can be any element or group of elements that occupy aphysical area on a specimen. For example, an analyte could be a parcelof land, a body of water or the like. When an analyte is imaged, eachanalyte will have some area. Thus, in many implementations, an analyteis not merely one pixel.

The distances between analytes can be described in any number of ways.In some implementations, the distances between analytes can be describedfrom the center of one analyte to the center of another analyte. Inother implementations, the distances can be described from the edge ofone analyte to the edge of another analyte, or between the outer-mostidentifiable points of each analyte. The edge of an analyte can bedescribed as the theoretical or actual physical boundary on a chip, orsome point inside the boundary of the analyte. In other implementations,the distances can be described in relation to a fixed point on thespecimen or in the image of the specimen.

Generally several implementations will be described herein with respectto a method of analysis. It will be understood that systems are alsoprovided for carrying out the methods in an automated or semi-automatedway. Accordingly, this disclosure provides neural network-based templategeneration and base calling systems, wherein the systems can include aprocessor; a storage device; and a program for image analysis, theprogram including instructions for carrying out one or more of themethods set forth herein. Accordingly, the methods set forth herein canbe carried out on a computer, for example, having components set forthherein or otherwise known in the art.

The methods and systems set forth herein are useful for analyzing any ofa variety of objects. Particularly useful objects are solid supports orsolid-phase surfaces with attached analytes. The methods and systems setforth herein provide advantages when used with objects having arepeating pattern of analytes in an xy plane. An example is a microarrayhaving an attached collection of cells, viruses, nucleic acids,proteins, antibodies, carbohydrates, small molecules (such as drugcandidates), biologically active molecules or other analytes ofinterest.

An increasing number of applications have been developed for arrays withanalytes having biological molecules such as nucleic acids andpolypeptides. Such microarrays typically include deoxyribonucleic acid(DNA) or ribonucleic acid (RNA) probes. These are specific fornucleotide sequences present in humans and other organisms. In certainapplications, for example, individual DNA or RNA probes can be attachedat individual analytes of an array. A test sample, such as from a knownperson or organism, can be exposed to the array, such that targetnucleic acids (e.g., gene fragments, mRNA, or amplicons thereof)hybridize to complementary probes at respective analytes in the array.The probes can be labeled in a target specific process (e.g., due tolabels present on the target nucleic acids or due to enzymatic labelingof the probes or targets that are present in hybridized form at theanalytes). The array can then be examined by scanning specificfrequencies of light over the analytes to identify which target nucleicacids are present in the sample.

Biological microarrays may be used for genetic sequencing and similarapplications. In general, genetic sequencing comprises determining theorder of nucleotides in a length of target nucleic acid, such as afragment of DNA or RNA. Relatively short sequences are typicallysequenced at each analyte, and the resulting sequence information may beused in various bioinformatics methods to logically fit the sequencefragments together so as to reliably determine the sequence of much moreextensive lengths of genetic material from which the fragments werederived. Automated, computer-based algorithms for characteristicfragments have been developed, and have been used more recently ingenome mapping, identification of genes and their function, and soforth. Microarrays are particularly useful for characterizing genomiccontent because a large number of variants are present and thissupplants the alternative of performing many experiments on individualprobes and targets. The microarray is an ideal format for performingsuch investigations in a practical manner.

Any of a variety of analyte arrays (also referred to as “microarrays”)known in the art can be used in a method or system set forth herein. Atypical array contains analytes, each having an individual probe or apopulation of probes. In the latter case, the population of probes ateach analyte is typically homogenous having a single species of probe.For example, in the case of a nucleic acid array, each analyte can havemultiple nucleic acid molecules each having a common sequence. However,in some implementations the populations at each analyte of an array canbe heterogeneous. Similarly, protein arrays can have analytes with asingle protein or a population of proteins typically, but not always,having the same amino acid sequence. The probes can be attached to thesurface of an array for example, via covalent linkage of the probes tothe surface or via non-covalent interaction(s) of the probes with thesurface. In some implementations, probes, such as nucleic acidmolecules, can be attached to a surface via a gel layer as described,for example, in U.S. patent application Ser. No. 13/784,368 and US Pat.App. Pub. No. 2011/0059865 A1, each of which is incorporated herein byreference.

Example arrays include, without limitation, a BeadChip Array availablefrom Illumina, Inc. (San Diego, Calif.) or others such as those whereprobes are attached to beads that are present on a surface (e.g. beadsin wells on a surface) such as those described in U.S. Pat. Nos.6,266,459; 6,355,431; 6,770,441; 6,859,570; or 7,622,294; or PCTPublication No. WO 00/63437, each of which is incorporated herein byreference. Further examples of commercially available microarrays thatcan be used include, for example, an Affymetrix® GeneChip® microarray orother microarray synthesized in accordance with techniques sometimesreferred to as VLSIPS™ (Very Large Scale Immobilized Polymer Synthesis)technologies. A spotted microarray can also be used in a method orsystem according to some implementations of the present disclosure. Anexample spotted microarray is a CodeLink™ Array available from AmershamBiosciences. Another microarray that is useful is one that ismanufactured using inkjet printing methods such as SurePrint™ Technologyavailable from Agilent Technologies.

Other useful arrays include those that are used in nucleic acidsequencing applications. For example, arrays having amplicons of genomicfragments (often referred to as clusters) are particularly useful suchas those described in Bentley et al., Nature 456:53-59 (2008), WO04/018497; WO 91/06678; WO 07/123744; U.S. Pat. Nos. 7,329,492;7,211,414; 7,315,019; 7,405,281, or 7,057,026; or US Pat. App. Pub. No.2008/0108082 A1, each of which is incorporated herein by reference.Another type of array that is useful for nucleic acid sequencing is anarray of particles produced from an emulsion PCR technique. Examples aredescribed in Dressman et al., Proc. Natl. Acad. Sci. USA 100:8817-8822(2003), WO 05/010145, US Pat. App. Pub. No. 2005/0130173 or US Pat. App.Pub. No. 2005/0064460, each of which is incorporated herein by referencein its entirety.

Arrays used for nucleic acid sequencing often have random spatialpatterns of nucleic acid analytes. For example, HiSeq or MiSeqsequencing platforms available from Illumina Inc. (San Diego, Calif.)utilize flow cells upon which nucleic acid arrays are formed by randomseeding followed by bridge amplification. However, patterned arrays canalso be used for nucleic acid sequencing or other analyticalapplications. Example patterned arrays, methods for their manufactureand methods for their use are set forth in U.S. Ser. No. 13/787,396;U.S. Ser. No. 13/783,043; U.S. Ser. No. 13/784,368; US Pat. App. Pub.No. 2013/0116153 A1; and US Pat. App. Pub. No. 2012/0316086 A1, each ofwhich is incorporated herein by reference. The analytes of suchpatterned arrays can be used to capture a single nucleic acid templatemolecule to seed subsequent formation of a homogenous colony, forexample, via bridge amplification. Such patterned arrays areparticularly useful for nucleic acid sequencing applications.

The size of an analyte on an array (or other object used in a method orsystem herein) can be selected to suit a particular application. Forexample, in some implementations, an analyte of an array can have a sizethat accommodates only a single nucleic acid molecule. A surface havinga plurality of analytes in this size range is useful for constructing anarray of molecules for detection at single molecule resolution. Analytesin this size range are also useful for use in arrays having analytesthat each contain a colony of nucleic acid molecules. Thus, the analytesof an array can each have an area that is no larger than about 1 mm², nolarger than about 500 μm², no larger than about 100 μm², no larger thanabout 10 μm², no larger than about 1 m², no larger than about 500 nm²,or no larger than about 100 nm², no larger than about 10 nm², no largerthan about 5 nm², or no larger than about 1 nm². Alternatively oradditionally, the analytes of an array will be no smaller than about 1mm², no smaller than about 500 μm², no smaller than about 100 μm², nosmaller than about 10 μm², no smaller than about 1 μm², no smaller thanabout 500 nm², no smaller than about 100 nm², no smaller than about 10nm², no smaller than about 5 nm², or no smaller than about 1 nm².Indeed, an analyte can have a size that is in a range between an upperand lower limit selected from those exemplified above. Although severalsize ranges for analytes of a surface have been exemplified with respectto nucleic acids and on the scale of nucleic acids, it will beunderstood that analytes in these size ranges can be used forapplications that do not include nucleic acids. It will be furtherunderstood that the size of the analytes need not necessarily beconfined to a scale used for nucleic acid applications.

For implementations that include an object having a plurality ofanalytes, such as an array of analytes, the analytes can be discrete,being separated with spaces between each other. An array useful in theinvention can have analytes that are separated by edge to edge distanceof at most 100 μm, 50 μm, 10 μm, 5 μm, 1 μm, 0.5 μm, or less.Alternatively or additionally, an array can have analytes that areseparated by an edge to edge distance of at least 0.5 m, 1 μm, 5 m, 10μm, 50 μm, 100 pm, or more. These ranges can apply to the average edgeto edge spacing for analytes as well as to the minimum or maximumspacing.

In some implementations the analytes of an army need not be discrete andinstead neighboring analytes can abut each other. Whether or not theanalytes are discrete, the size of the analytes and/or pitch of theanalytes can vary such that arrays can have a desired density. Forexample, the average analyte pitch in a regular pattern can be at most100 μm, 50 μm, 10 μm, 5 pm, 1 μm, 0.5 μm, or less. Alternatively oradditionally, the average analyte pitch in a regular pattern can be atleast 0.5 μm, 1 μm, 5 μm, 10 μm, 50 μm, 100 μm, or more. These rangescan apply to the maximum or minimum pitch for a regular pattern as well.For example, the maximum analyte pitch for a regular pattern can be atmost 100 μm, 50 pm, 10 μm, 5 μm, 1 μm, 0.5 pm, or less; and/or theminimum analyte pitch in a regular pattern can be at least 0.5 μm, 1 μm,5 μm, 10 μm, 50 μm, 100 μm, or more.

The density of analytes in an array can also be understood in terms ofthe number of analytes present per unit area. For example, the averagedensity of analytes for an array can be at least about 1×10³analytes/mm², 1×10⁴ analytes/mm², 1×10⁵ analytes/mm², 1×10⁶analytes/mm², 1×10⁷ analytes/mm², 1×10⁸ analytes/mm², or 1×10⁹analytes/mm², or higher. Alternatively or additionally the averagedensity of analytes for an array can be at most about 1×10⁹analytes/mm², 1×10⁸ analytes/mm², 1×10⁷ analytes/mm², 1×10⁶analytes/mm², 1×10⁵ analytes/mm², 1×10⁴ analytes/mm², or 1×10³analytes/mm², or less.

The above ranges can apply to all or part of a regular patternincluding, for example, all or part of an array of analytes.

The analytes in a pattern can have any of a variety of shapes. Forexample, when observed in a two dimensional plane, such as on thesurface of an array, the analytes can appear rounded, circular, oval,rectangular, square, symmetric, asymmetric, triangular, polygonal, orthe like. The analytes can be arranged in a regular repeating patternincluding, for example, a hexagonal or rectilinear pattern. A patterncan be selected to achieve a desired level of packing. For example,round analytes are optimally packed in a hexagonal arrangement. Ofcourse other packing arrangements can also be used for round analytesand vice versa.

A pattern can be characterized in terms of the number of analytes thatare present in a subset that forms the smallest geometric unit of thepattern. The subset can include, for example, at least about 2, 3, 4, 5,6, 10 or more analytes. Depending upon the size and density of theanalytes the geometric unit can occupy an area of less than 1 mm², 500μm², 100 μm², 50 μm², 10 μm², 1 μm², 500 nm², 100 nm², 50 nm², 10 nm²,or less. Alternatively or additionally, the geometric unit can occupy anarea of greater than 10 nm², 50 nm², 100 nm², 500 nm², 1 μm², 10 μm², 50μm², 100 μm², 500 μm², 1 mm², or more. Characteristics of the analytesin a geometric unit, such as shape, size, pitch and the like, can beselected from those set forth herein more generally with regard toanalytes in an array or pattern.

An array having a regular pattern of analytes can be ordered withrespect to the relative locations of the analytes but random withrespect to one or more other characteristic of each analyte. Forexample, in the case of a nucleic acid array, the nuclei acid analytescan be ordered with respect to their relative locations but random withrespect to one's knowledge of the sequence for the nucleic acid speciespresent at any particular analyte. As a more specific example, nucleicacid arrays formed by seeding a repeating pattern of analytes withtemplate nucleic acids and amplifying the template at each analyte toform copies of the template at the analyte (e.g., via clusteramplification or bridge amplification) will have a regular pattern ofnucleic acid analytes but will be random with regard to the distributionof sequences of the nucleic acids across the array. Thus, detection ofthe presence of nucleic acid material generally on the array can yield arepeating pattern of analytes, whereas sequence specific detection canyield non-repeating distribution of signals across the array.

It will be understood that the description herein of patterns, order,randomness and the like pertain not only to analytes on objects, such asanalytes on arrays, but also to analytes in images. As such, patterns,order, randomness and the like can be present in any of a variety offormats that are used to store, manipulate or communicate image dataincluding, but not limited to, a computer readable medium or computercomponent such as a graphical user interface or other output device.

As used herein, the term “image” is intended to mean a representation ofall or part of an object. The representation can be an opticallydetected reproduction. For example, an image can be obtained fromfluorescent, luminescent, scatter, or absorption signals. The part ofthe object that is present in an image can be the surface or other xyplane of the object. Typically, an image is a 2 dimensionalrepresentation, but in some cases information in the image can bederived from 3 or more dimensions. An image need not include opticallydetected signals. Non-optical signals can be present instead. An imagecan be provided in a computer readable format or medium such as one ormore of those set forth elsewhere herein.

As used herein, “image” refers to a reproduction or representation of atleast a portion of a specimen or other object. In some implementations,the reproduction is an optical reproduction, for example, produced by acamera or other optical detector. The reproduction can be a non-opticalreproduction, for example, a representation of electrical signalsobtained from an array of nanopore analytes or a representation ofelectrical signals obtained from an ion-sensitive CMOS detector. Inparticular implementations non-optical reproductions can be excludedfrom a method or apparatus set forth herein. An image can have aresolution capable of distinguishing analytes of a specimen that arepresent at any of a variety of spacings including, for example, thosethat are separated by less than 100 μm, 50 μm, 10 μm, 5 μm, 1 μm or 0.5μm.

As used herein, “acquiring”, “acquisition” and like terms refer to anypart of the process of obtaining an image file. In some implementations,data acquisition can include generating an image of a specimen, lookingfor a signal in a specimen, instructing a detection device to look foror generate an image of a signal, giving instructions for furtheranalysis or transformation of an image file, and any number oftransformations or manipulations of an image file.

As used herein, the term “template” refers to a representation of thelocation or relation between signals or analytes. Thus, in someimplementations, a template is a physical grid with a representation ofsignals corresponding to analytes in a specimen. In someimplementations, a template can be a chart, table, text file or othercomputer file indicative of locations corresponding to analytes. Inimplementations presented herein, a template is generated in order totrack the location of analytes of a specimen across a set of images ofthe specimen captured at different reference points. For example, atemplate could be a set of x,y coordinates or a set of values thatdescribe the direction and/or distance of one analyte with respect toanother analyte.

As used herein, the term “specimen” can refer to an object or area of anobject of which an image is captured. For example, in implementationswhere images are taken of the surface of the earth, a parcel of land canbe a specimen. In other implementations where the analysis of biologicalmolecules is performed in a flow cell, the flow cell may be divided intoany number of subdivisions, each of which may be a specimen. Forexample, a flow cell may be divided into various flow channels or lanes,and each lane can be further divided into 2, 3, 4, 5, 6, 7, 8, 9, 10,20, 30, 40, 50, 60 70, 80, 90, 100, 110, 120, 140, 160, 180, 200, 400,600, 800, 1000 or more separate regions that are imaged. One example ofa flow cell has 8 lanes, with each lane divided into 120 specimens ortiles. In another implementation, a specimen may be made up of aplurality of tiles or even an entire flow cell. Thus, the image of eachspecimen can represent a region of a larger surface that is imaged.

It will be appreciated that references to ranges and sequential numberlists described herein include not only the enumerated number but allreal numbers between the enumerated numbers.

As used herein, a “reference point” refers to any temporal or physicaldistinction between images. In a preferred implementation, a referencepoint is a time point. In a more preferred implementation, a referencepoint is a time point or cycle during a sequencing reaction. However,the term “reference point” can include other aspects that distinguish orseparate images, such as angle, rotational, temporal, or other aspectsthat can distinguish or separate images.

As used herein, a “subset of images” refers to a group of images withina set. For example, a subset may contain 1, 2, 3, 4, 6, 8, 10, 12, 14,16, 18, 20, 30, 40, 50, 60 or any number of images selected from a setof images. In particular implementations, a subset may contain no morethan 1, 2, 3, 4, 6, 8, 10, 12, 14, 16, 18, 20, 30, 40, 50, 60 or anynumber of images selected from a set of images. In a preferredimplementation, images are obtained from one or more sequencing cycleswith four images correlated to each cycle. Thus, for example, a subsetcould be a group of 16 images obtained through four cycles.

A base refers to a nucleotide base or nucleotide, A (adenine), C(cytosine), T (thymine), or G (guanine). This application uses “base(s)”and “nucleotide(s)” interchangeably.

The term “chromosome” refers to the heredity-bearing gene carrier of aliving cell, which is derived from chromatin strands comprising DNA andprotein components (especially histones). The conventionalinternationally recognized individual human genome chromosome numberingsystem is employed herein.

The term “site” refers to a unique position (e.g., chromosome ID,chromosome position and orientation) on a reference genome. In someimplementations, a site may be a residue, a sequence tag, or a segment'sposition on a sequence. The term “locus” may be used to refer to thespecific location of a nucleic acid sequence or polymorphism on areference chromosome.

The term “sample” herein refers to a sample, typically derived from abiological fluid, cell, tissue, organ, or organism containing a nucleicacid or a mixture of nucleic acids containing at least one nucleic acidsequence that is to be sequenced and/or phased. Such samples include,but are not limited to sputum/oral fluid, amniotic fluid, blood, a bloodfraction, fine needle biopsy samples (e.g., surgical biopsy, fine needlebiopsy, etc.), urine, peritoneal fluid, pleural fluid, tissue explant,organ culture and any other tissue or cell preparation, or fraction orderivative thereof or isolated therefrom. Although the sample is oftentaken from a human subject (e.g., patient), samples can be taken fromany organism having chromosomes, including, but not limited to dogs,cats, horses, goats, sheep, cattle, pigs, etc. The sample may be useddirectly as obtained from the biological source or following apretreatment to modify the character of the sample. For example, suchpretreatment may include preparing plasma from blood, diluting viscousfluids and so forth. Methods of pretreatment may also involve, but arenot limited to, filtration, precipitation, dilution, distillation,mixing, centrifugation, freezing, lyophilization, concentration,amplification, nucleic acid fragmentation, inactivation of interferingcomponents, the addition of reagents, lysing, etc.

The term “sequence” includes or represents a strand of nucleotidescoupled to each other. The nucleotides may be based on DNA or RNA. Itshould be understood that one sequence may include multiplesub-sequences. For example, a single sequence (e.g., of a PCR amplicon)may have 350 nucleotides. The sample read may include multiplesub-sequences within these 350 nucleotides. For instance, the sampleread may include first and second flanking subsequences having, forexample, 20-50 nucleotides. The first and second flanking sub-sequencesmay be located on either side of a repetitive segment having acorresponding sub-sequence (e.g., 40-100 nucleotides). Each of theflanking sub-sequences may include (or include portions of) a primersub-sequence (e.g., 10-30 nucleotides). For ease of reading, the term“sub-sequence” will be referred to as “sequence,” but it is understoodthat two sequences are not necessarily separate from each other on acommon strand. To differentiate the various sequences described herein,the sequences may be given different labels (e.g., target sequence,primer sequence, flanking sequence, reference sequence, and the like).Other terms, such as “allele,” may be given different labels todifferentiate between like objects. The application uses “read(s)” and“sequence read(s)” interchangeably.

The term “paired-end sequencing” refers to sequencing methods thatsequence both ends of a target fragment. Paired-end sequencing mayfacilitate detection of genomic rearrangements and repetitive segments,as well as gene fusions and novel transcripts. Methodology forpaired-end sequencing are described in PCT publication WO07010252, PCTapplication Serial No. PCTGB2007/003798 and US patent applicationpublication US 2009/0088327, each of which is incorporated by referenceherein. In one example, a series of operations may be performed asfollows; (a) generate clusters of nucleic acids; (b) linearize thenucleic acids; (c) hybridize a first sequencing primer and carry outrepeated cycles of extension, scanning and deblocking, as set forthabove; (d) “invert” the target nucleic acids on the flow cell surface bysynthesizing a complimentary copy; (e) linearize the resynthesizedstrand; and (f) hybridize a second sequencing primer and carry outrepeated cycles of extension, scanning and deblocking, as set forthabove. The inversion operation can be carried out be delivering reagentsas set forth above for a single cycle of bridge amplification.

The term “reference genome” or “reference sequence” refers to anyparticular known genome sequence, whether partial or complete, of anyorganism which may be used to reference identified sequences from asubject. For example, a reference genome used for human subjects as wellas many other organisms is found at the National Center forBiotechnology Information at ncbi.nlm.nih.gov. A “genome” refers to thecomplete genetic information of an organism or virus, expressed innucleic acid sequences. A genome includes both the genes and thenoncoding sequences of the DNA. The reference sequence may be largerthan the reads that are aligned to it. For example, it may be at leastabout 100 times larger, or at least about 1000 times larger, or at leastabout 10,000 times larger, or at least about 105 times larger, or atleast about 106 times larger, or at least about 107 times larger. In oneexample, the reference genome sequence is that of a full length humangenome. In another example, the reference genome sequence is limited toa specific human chromosome such as chromosome 13. In someimplementations, a reference chromosome is a chromosome sequence fromhuman genome version hg19. Such sequences may be referred to aschromosome reference sequences, although the term reference genome isintended to cover such sequences. Other examples of reference sequencesinclude genomes of other species, as well as chromosomes,sub-chromosomal regions (such as strands), etc., of any species. Invarious implementations, the reference genome is a consensus sequence orother combination derived from multiple individuals. However, in certainapplications, the reference sequence may be taken from a particularindividual. In other implementations, the “genome” also covers so-called“graph genomes”, which use a particular storage format andrepresentation of the genome sequence. In one implementation, graphgenomes store data in a linear file. In another implementation, thegraph genomes refer to a representation where alternative sequences(e.g., different copies of a chromosome with small differences) arestored as different paths in a graph. Additional information regardinggraph genome implementations can be found inhttps://www.biorxiv.org/content/biorxiv/early/2018/03/20/194530.full.pdf,the content of which is hereby incorporated herein by reference in itsentirety.

The term “read” refer to a collection of sequence data that describes afragment of a nucleotide sample or reference. The term “read” may referto a sample read and/or a reference read. Typically, though notnecessarily, a read represents a short sequence of contiguous base pairsin the sample or reference. The read may be represented symbolically bythe base pair sequence (in ACTG) of the sample or reference fragment. Itmay be stored in a memory device and processed as appropriate todetermine whether the read matches a reference sequence or meets othercriteria. A read may be obtained directly from a sequencing apparatus orindirectly from stored sequence information concerning the sample. Insome cases, a read is a DNA sequence of sufficient length (e.g., atleast about 25 bp) that can be used to identify a larger sequence orregion, e.g., that can be aligned and specifically assigned to achromosome or genomic region or gene.

Next-generation sequencing methods include, for example, sequencing bysynthesis technology (Illumina), pyrosequencing (454), ion semiconductortechnology (Ion Torrent sequencing), single-molecule real-timesequencing and sequencing by ligation (SOLD sequencing). Depending onthe sequencing methods, the length of each read may vary from about 30bp to more than 10,000 bp. For example, the DNA sequencing method usingSOLID sequencer generates nucleic acid reads of about 50 bp. For anotherexample, Ion Torrent Sequencing generates nucleic acid reads of up to400 bp and 454 pyrosequencing generates nucleic acid reads of about 700bp. For yet another example, single-molecule real-time sequencingmethods may generate reads of 10,000 bp to 15,000 bp. Therefore, incertain implementations, the nucleic acid sequence reads have a lengthof 30-100 bp, 50-200 bp, or 50-400 bp.

The terms “sample read”, “sample sequence” or “sample fragment” refer tosequence data for a genomic sequence of interest from a sample. Forexample, the sample read comprises sequence data from a PCR ampliconhaving a forward and reverse primer sequence. The sequence data can beobtained from any select sequence methodology. The sample read can be,for example, from a sequencing-by-synthesis (SBS) reaction, asequencing-by-ligation reaction, or any other suitable sequencingmethodology for which it is desired to determine the length and/oridentity of a repetitive element. The sample read can be a consensus(e.g., averaged or weighted) sequence derived from multiple samplereads. In certain implementations, providing a reference sequencecomprises identifying a locus-of-interest based upon the primer sequenceof the PCR amplicon.

The term “raw fragment” refers to sequence data for a portion of agenomic sequence of interest that at least partially overlaps adesignated position or secondary position of interest within a sampleread or sample fragment. Non-limiting examples of raw fragments includea duplex stitched fragment, a simplex stitched fragment, a duplexun-stitched fragment and a simplex un-stitched fragment. The term “raw”is used to indicate that the raw fragment includes sequence data havingsome relation to the sequence data in a sample read, regardless ofwhether the raw fragment exhibits a supporting variant that correspondsto and authenticates or confirms a potential variant in a sample read.The term “raw fragment” does not indicate that the fragment necessarilyincludes a supporting variant that validates a variant call in a sampleread. For example, when a sample read is determined by a variant callapplication to exhibit a first variant, the variant call application maydetermine that one or more raw fragments lack a corresponding type of“supporting” variant that may otherwise be expected to occur given thevariant in the sample read.

The terms “mapping”, “aligned,” “alignment,” or “aligning” refer to theprocess of comparing a read or tag to a reference sequence and therebydetermining whether the reference sequence contains the read sequence.If the reference sequence contains the read, the read may be mapped tothe reference sequence or, in certain implementations, to a particularlocation in the reference sequence. In some cases, alignment simplytells whether or not a read is a member of a particular referencesequence (i.e., whether the read is present or absent in the referencesequence). For example, the alignment of a read to the referencesequence for human chromosome 13 will tell whether the read is presentin the reference sequence for chromosome 13. A tool that provides thisinformation may be called a set membership tester. In some cases, analignment additionally indicates a location in the reference sequencewhere the read or tag maps to. For example, if the reference sequence isthe whole human genome sequence, an alignment may indicate that a readis present on chromosome 13, and may further indicate that the read ison a particular strand and/or site of chromosome 13.

The term “indel” refers to the insertion and/or the deletion of bases inthe DNA of an organism. A micro-indel represents an indel that resultsin a net change of 1 to 50 nucleotides. In coding regions of the genome,unless the length of an indel is a multiple of 3, it will produce aframeshift mutation. Indels can be contrasted with point mutations. Anindel inserts and deletes nucleotides from a sequence, while a pointmutation is a form of substitution that replaces one of the nucleotideswithout changing the overall number in the DNA. Indels can also becontrasted with a Tandem Base Mutation (TBM), which may be defined assubstitution at adjacent nucleotides (primarily substitutions at twoadjacent nucleotides, but substitutions at three adjacent nucleotideshave been observed.

The term “variant” refers to a nucleic acid sequence that is differentfrom a nucleic acid reference. Typical nucleic acid sequence variantincludes without limitation single nucleotide polymorphism (SNP), shortdeletion and insertion polymorphisms (Indel), copy number variation(CNV), microsatellite markers or short tandem repeats and structuralvariation. Somatic variant calling is the effort to identify variantspresent at low frequency in the DNA sample. Somatic variant calling isof interest in the context of cancer treatment. Cancer is caused by anaccumulation of mutations in DNA. A DNA sample from a tumor is generallyheterogeneous, including some normal cells, some cells at an early stageof cancer progression (with fewer mutations), and some late-stage cells(with more mutations). Because of this heterogeneity, when sequencing atumor (e.g., from an FFPE sample), somatic mutations will often appearat a low frequency. For example, a SNV might be seen in only 10% of thereads covering a given base. A variant that is to be classified assomatic or germline by the variant classifier is also referred to hereinas the “variant under test”.

The term “noise” refers to a mistaken variant call resulting from one ormore errors in the sequencing process and/or in the variant callapplication.

The term “variant frequency” represents the relative frequency of anallele (variant of a gene) at a particular locus in a population,expressed as a fraction or percentage. For example, the fraction orpercentage may be the fraction of all chromosomes in the population thatcarry that allele. By way of example, sample variant frequencyrepresents the relative frequency of an allele/variant at a particularlocus/position along a genomic sequence of interest over a “population”corresponding to the number of reads and/or samples obtained for thegenomic sequence of interest from an individual. As another example, abaseline variant frequency represents the relative frequency of anallele/variant at a particular locus/position along one or more baselinegenomic sequences where the “population” corresponding to the number ofreads and/or samples obtained for the one or more baseline genomicsequences from a population of normal individuals.

The term “variant allele frequency (VAF)” refers to the percentage ofsequenced reads observed matching the variant divided by the overallcoverage at the target position. VAF is a measure of the proportion ofsequenced reads carrying the variant.

The terms “position”, “designated position”, and “locus” refer to alocation or coordinate of one or more nucleotides within a sequence ofnucleotides. The terms “position”, “designated position”, and “locus”also refer to a location or coordinate of one or more base pairs in asequence of nucleotides.

The term “haplotype” refers to a combination of alleles at adjacentsites on a chromosome that are inherited together. A haplotype may beone locus, several loci, or an entire chromosome depending on the numberof recombination events that have occurred between a given set of loci,if any occurred.

The term “threshold” herein refers to a numeric or non-numeric valuethat is used as a cutoff to characterize a sample, a nucleic acid, orportion thereof (e.g., a read). A threshold may be varied based uponempirical analysis. The threshold may be compared to a measured orcalculated value to determine whether the source giving rise to suchvalue suggests should be classified in a particular manner. Thresholdvalues can be identified empirically or analytically. The choice of athreshold is dependent on the level of confidence that the user wishesto have to make the classification. The threshold may be chosen for aparticular purpose (e.g., to balance sensitivity and selectivity). Asused herein, the term “threshold” indicates a point at which a course ofanalysis may be changed and/or a point at which an action may betriggered. A threshold is not required to be a predetermined number.Instead, the threshold may be, for instance, a function that is based ona plurality of factors. The threshold may be adaptive to thecircumstances. Moreover, a threshold may indicate an upper limit, alower limit, or a range between limits.

In some implementations, a metric or score that is based on sequencingdata may be compared to the threshold. As used herein, the terms“metric” or “score” may include values or results that were determinedfrom the sequencing data or may include functions that are based on thevalues or results that were determined from the sequencing data. Like athreshold, the metric or score may be adaptive to the circumstances. Forinstance, the metric or score may be a normalized value. As an exampleof a score or metric, one or more implementations may use count scoreswhen analyzing the data. A count score may be based on number of samplereads. The sample reads may have undergone one or more filtering stagessuch that the sample reads have at least one common characteristic orquality. For example, each of the sample reads that are used todetermine a count score may have been aligned with a reference sequenceor may be assigned as a potential allele. The number of sample readshaving a common characteristic may be counted to determine a read count.Count scores may be based on the read count. In some implementations,the count score may be a value that is equal to the read count. In otherimplementations, the count score may be based on the read count andother information. For example, a count score may be based on the readcount for a particular allele of a genetic locus and a total number ofreads for the genetic locus. In some implementations, the count scoremay be based on the read count and previously-obtained data for thegenetic locus. In some implementations, the count scores may benormalized scores between predetermined values. The count score may alsobe a function of read counts from other loci of a sample or a functionof read counts from other samples that were concurrently run with thesample-of-interest. For instance, the count score may be a function ofthe read count of a particular allele and the read counts of other lociin the sample and/or the read counts from other samples. As one example,the read counts from other loci and/or the read counts from othersamples may be used to normalize the count score for the particularallele.

The terms “coverage” or “fragment coverage” refer to a count or othermeasure of a number of sample reads for the same fragment of a sequence.A read count may represent a count of the number of reads that cover acorresponding fragment. Alternatively, the coverage may be determined bymultiplying the read count by a designated factor that is based onhistorical knowledge, knowledge of the sample, knowledge of the locus,etc.

The term “read depth” (conventionally a number followed by “x”) refersto the number of sequenced reads with overlapping alignment at thetarget position. This is often expressed as an average or percentageexceeding a cutoff over a set of intervals (such as exons, genes, orpanels). For example, a clinical report might say that a panel averagecoverage is 1,105x with 98% of targeted bases covered >100×.

The terms “base call quality score” or “Q score” refer to a PHRED-scaledprobability ranging from 0-50 inversely proportional to the probabilitythat a single sequenced base is correct. For example, a T base call withQ of 20 is considered likely correct with a probability of 99.99%. Anybase call with Q<20 should be considered low quality, and any variantidentified where a substantial proportion of sequenced reads supportingthe variant are of low quality should be considered potentially falsepositive.

The terms “variant reads” or “variant read number” refer to the numberof sequenced reads supporting the presence of the variant.

Regarding “strandedness” (or DNA strandedness), the genetic message inDNA can be represented as a string of the letters A, G, C, and T. Forexample, 5′-AGGACA-3′. Often, the sequence is written in the directionshown here, i.e., with the 5′ end to the left and the 3′ end to theright. DNA may sometimes occur as single-stranded molecule (as incertain viruses), but normally we find DNA as a double-stranded unit. Ithas a double helical structure with two antiparallel strands. In thiscase, the word “antiparallel” means that the two strands run inparallel, but have opposite polarity. The double-stranded DNA is heldtogether by pairing between bases and the pairing is always such thatadenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine(G). This pairing is referred to as complementarity, and one strand ofDNA is said to be the complement of the other. The double-stranded DNAmay thus be represented as two strings, like this: 5′-AGGACA-3′ and3′-TCCTGT-5′. Note that the two strands have opposite polarity.Accordingly, the strandedness of the two DNA strands can be referred toas the reference strand and its complement, forward and reverse strands,top and bottom strands, sense and antisense strands, or Watson and Crickstrands.

The reads alignment (also called reads mapping) is the process offiguring out where in the genome a sequence is from. Once the alignmentis performed, the “mapping quality” or the “mapping quality score(MAPQ)” of a given read quantifies the probability that its position onthe genome is correct. The mapping quality is encoded in the phred scalewhere P is the probability that the alignment is not correct. Theprobability is calculated as: P=10^((−MAQ/10)), where MAPQ is themapping quality. For example, a mapping quality of 40=10 to the power of−4, meaning that there is a 0.01% chance that the read was alignedincorrectly. The mapping quality is therefore associated with severalalignment factors, such as the base quality of the read, the complexityof the reference genome, and the paired-end information. Regarding thefirst, if the base quality of the read is low, it means that theobserved sequence might be wrong and thus its alignment is wrong.Regarding the second, the mappability refers to the complexity of thegenome. Repeated regions are more difficult to map and reads falling inthese regions usually get low mapping quality. In this context, the MAPQreflects the fact that the reads are not uniquely aligned and that theirreal origin cannot be determined. Regarding the third, in case ofpaired-end sequencing data, concordant pairs are more likely to be wellaligned. The higher is the mapping quality, the better is the alignment.A read aligned with a good mapping quality usually means that the readsequence was good and was aligned with few mismatches in a highmappability region. The MAPQ value can be used as a quality control ofthe alignment results. The proportion of reads aligned with an MAPQhigher than 20 is usually for downstream analysis.

As used herein, a “signal” refers to a detectable event such as anemission, preferably light emission, for example, in an image. Thus, inpreferred implementations, a signal can represent any detectable lightemission that is captured in an image (i.e., a “spot”). Thus, as usedherein, “signal” can refer to both an actual emission from an analyte ofthe specimen, and can refer to a spurious emission that does notcorrelate to an actual analyte. Thus, a signal could arise from noiseand could be later discarded as not representative of an actual analyteof a specimen.

As used herein, the term “clump” refers to a group of signals. Inparticular implementations, the signals are derived from differentanalytes. In a preferred implementation, a signal clump is a group ofsignals that cluster together. In a more preferred implementation, asignal clump represents a physical region covered by one amplifiedoligonucleotide. Each signal clump should be ideally observed as severalsignals (one per template cycle, and possibly more due to cross-talk).Accordingly, duplicate signals are detected where two (or more) signalsare included in a template from the same clump of signals.

As used herein, terms such as “minimum,” “maximum,” “minimize,”“maximize” and grammatical variants thereof can include values that arenot the absolute maxima or minima. In some implementations, the valuesinclude near maximum and near minimum values. In other implementations,the values can include local maximum and/or local minimum values. Insome implementations, the values include only absolute maximum orminimum values.

As used herein, “cross-talk” refers to the detection of signals in oneimage that are also detected in a separate image. In a preferredimplementation, cross-talk can occur when an emitted signal is detectedin two separate detection channels. For example, where an emitted signaloccurs in one color, the emission spectrum of that signal may overlapwith another emitted signal in another color. In a preferredimplementation, fluorescent molecules used to indicate the presence ofnucleotide bases A, C, G and T are detected in separate channels.However, because the emission spectra of A and C overlap, some of the Ccolor signal may be detected during detection using the A color channel.Accordingly, cross-talk between the A and C signals allows signals fromone color image to appear in the other color image. In someimplementations, G and T cross-talk. In some implementations, the amountof cross-talk between channels is asymmetric. It will be appreciatedthat the amount of cross-talk between channels can be controlled by,among other things, the selection of signal molecules having anappropriate emission spectrum as well as selection of the size andwavelength range of the detection channel.

As used herein, “register”, “registering”, “registration” and like termsrefer to any process to correlate signals in an image or data set from afirst time point or perspective with signals in an image or data setfrom another time point or perspective. For example, registration can beused to align signals from a set of images to form a template. Inanother example, registration can be used to align signals from otherimages to a template. One signal may be directly or indirectlyregistered to another signal. For example, a signal from image “S” maybe registered to image “G” directly. As another example, a signal fromimage “N” may be directly registered to image “G”, or alternatively, thesignal from image “N” may be registered to image “S”, which haspreviously been registered to image “G”. Thus, the signal from image “N”is indirectly registered to image “G”.

As used herein, the term “fiducial” is intended to mean adistinguishable point of reference in or on an object. The point ofreference can be, for example, a mark, second object, shape, edge, area,irregularity, channel, pit, post or the like. The point of reference canbe present in an image of the object or in another data set derived fromdetecting the object. The point of reference can be specified by an xand/or y coordinate in a plane of the object. Alternatively oradditionally, the point of reference can be specified by a z coordinatethat is orthogonal to the xy plane, for example, being defined by therelative locations of the object and a detector. One or more coordinatesfor a point of reference can be specified relative to one or more otheranalytes of an object or of an image or other data set derived from theobject.

As used herein, the term “optical signal” is intended to include, forexample, fluorescent, luminescent, scatter, or absorption signals.Optical signals can be detected in the ultraviolet (UV) range (about 200to 390 inn), visible (VIS) range (about 391 to 770 nm), infrared (IR)range (about 0.771 to 25 microns), or other range of the electromagneticspectrum. Optical signals can be detected in a way that excludes all orpart of one or more of these ranges.

As used herein, the term “signal level” is intended to mean an amount orquantity of detected energy or coded information that has a desired orpredefined characteristic. For example, an optical signal can bequantified by one or more of intensity, wavelength, energy, frequency,power, luminance or the like. Other signals can be quantified accordingto characteristics such as voltage, current, electric field strength,magnetic field strength, frequency, power, temperature, etc. Absence ofsignal is understood to be a signal level of zero or a signal level thatis not meaningfully distinguished from noise.

As used herein, the term “simulate” is intended to mean creating arepresentation or model of a physical thing or action that predictscharacteristics of the thing or action. The representation or model canin many cases be distinguishable from the thing or action. For example,the representation or model can be distinguishable from a thing withrespect to one or more characteristic such as color, intensity ofsignals detected from all or part of the thing, size, or shape. Inparticular implementations, the representation or model can beidealized, exaggerated, muted, or incomplete when compared to the thingor action. Thus, in some implementations, a representation of model canbe distinguishable from the thing or action that it represents, forexample, with respect to at least one of the characteristics set forthabove. The representation or model can be provided in a computerreadable format or medium such as one or more of those set forthelsewhere herein.

As used herein, the term “specific signal” is intended to mean detectedenergy or coded information that is selectively observed over otherenergy or information such as background energy or information. Forexample, a specific signal can be an optical signal detected at aparticular intensity, wavelength or color; an electrical signal detectedat a particular frequency, power or field strength; or other signalsknown in the art pertaining to spectroscopy and analytical detection.

As used herein, the term “swath” is intended to mean a rectangularportion of an object. The swath can be an elongated strip that isscanned by relative movement between the object and a detector in adirection that is parallel to the longest dimension of the strip.Generally, the width of the rectangular portion or strip will beconstant along its full length. Multiple swaths of an object can beparallel to each other. Multiple swaths of an object can be adjacent toeach other, overlapping with each other, abutting each other, orseparated from each other by an interstitial area.

As used herein, the term “variance” is intended to mean a differencebetween that which is expected and that which is observed or adifference between two or more observations. For example, variance canbe the discrepancy between an expected value and a measured value.Variance can be represented using statistical functions such as standarddeviation, the square of standard deviation, coefficient of variation orthe like.

As used herein, the term “xy coordinates” is intended to meaninformation that specifies location, size, shape, and/or orientation inan xy plane. The information can be, for example, numerical coordinatesin a Cartesian system. The coordinates can be provided relative to oneor both of the x and y axes or can be provided relative to anotherlocation in the xy plane. For example, coordinates of an analyte of anobject can specify the location of the analyte relative to location of afiducial or other analyte of the object.

As used herein, the term “xy plane” is intended to mean a 2 dimensionalarea defined by straight line axes x and y. When used in reference to adetector and an object observed by the detector, the area can be furtherspecified as being orthogonal to the direction of observation betweenthe detector and object being detected.

As used herein, the term “z coordinate” is intended to mean informationthat specifies the location of a point, line or area along an axes thatis orthogonal to an xy plane. In particular implementations, the z axisis orthogonal to an area of an object that is observed by a detector.For example, the direction of focus for an optical system may bespecified along the z axis.

In some implementations, acquired signal data is transformed using anaffine transformation. In some such implementations, template generationmakes use of the fact that the affine transforms between color channelsare consistent between runs. Because of this consistency, a set ofdefault offsets can be used when determining the coordinates of theanalytes in a specimen. For example, a default offsets file can containthe relative transformation (shift, scale, skew) for the differentchannels relative to one channel, such as the A channel. In otherimplementations, however, the offsets between color channels driftduring a run and/or between runs, making offset-driven templategeneration difficult. In such implementations, the methods and systemsprovided herein can utilize offset-less template generation, which isdescribed further below.

In some implementations of the above implementations, the system cancomprise a flow cell. In some implementations, the flow cell compriseslanes, or other configurations, of tiles, wherein at least some of thetiles comprise one or more arrays of analytes. In some implementations,the analytes comprise a plurality of molecules such as nucleic acids. Incertain aspects, the flow cell is configured to deliver a labelednucleotide base to an array of nucleic acids, thereby extending a primerhybridized to a nucleic acid within an analyte so as to produce a signalcorresponding to an analyte comprising the nucleic acid. In preferredimplementations, the nucleic acids within an analyte are identical orsubstantially identical to each other.

In some of the systems for image analysis described herein, each imagein the set of images includes color signals, wherein a different colorcorresponds to a different nucleotide base. In some implementations,each image of the set of images comprises signals having a single colorselected from at least four different colors. In some implementations,each image in the set of images comprises signals having a single colorselected from four different colors. In some of the systems describedherein, nucleic acids can be sequenced by providing four differentlabeled nucleotide bases to the array of molecules so as to produce fourdifferent images, each image comprising signals having a single color,wherein the signal color is different for each of the four differentimages, thereby producing a cycle of four color images that correspondsto the four possible nucleotides present at a particular position in thenucleic acid. In certain aspects, the system comprises a flow cell thatis configured to deliver additional labeled nucleotide bases to thearray of molecules, thereby producing a plurality of cycles of colorimages.

In preferred implementations, the methods provided herein can includedetermining whether a processor is actively acquiring data or whetherthe processor is in a low activity state. Acquiring and storing largenumbers of high-quality images typically requires massive amounts ofstorage capacity. Additionally, once acquired and stored, the analysisof image data can become resource intensive and can interfere withprocessing capacity of other functions, such as ongoing acquisition andstorage of additional image data. Accordingly, as used herein, the termlow activity state refers to the processing capacity of a processor at agiven time. In some implementations, a low activity state occurs when aprocessor is not acquiring and/or storing data. In some implementations,a low activity state occurs when some data acquisition and/or storage istaking place, but additional processing capacity remains such that imageanalysis can occur at the same time without interfering with otherfunctions.

As used herein, “identifying a conflict” refers to identifying asituation where multiple processes compete for resources. In some suchimplementations, one process is given priority over another process. Insome implementations, a conflict may relate to the need to give priorityfor allocation of time, processing capacity, storage capacity or anyother resource for which priority is given. Thus, in someimplementations, where processing time or capacity is to be distributedbetween two processes such as either analyzing a data set and acquiringand/or storing the data set, a conflict between the two processes existsand can be resolved by giving priority to one of the processes.

Also provided herein are systems for performing image analysis. Thesystems can include a processor; a storage capacity; and a program forimage analysis, the program comprising instructions for processing afirst data set for storage and the second data set for analysis, whereinthe processing comprises acquiring and/or storing the first data set onthe storage device and analyzing the second data set when the processoris not acquiring the first data set. In certain aspects, the programincludes instructions for identifying at least one instance of aconflict between acquiring and/or storing the first data set andanalyzing the second data set; and resolving the conflict in favor ofacquiring and/or storing image data such that acquiring and/or storingthe first data set is given priority. In certain aspects, the first dataset comprises image files obtained from an optical imaging device. Incertain aspects, the system further comprises an optical imaging device.In some implementations, the optical imaging device comprises a lightsource and a detection device.

As used herein, the term “program” refers to instructions or commands toperform a task or process. The term “program” can be usedinterchangeably with the term module. In certain implementations, aprogram can be a compilation of various instructions executed under thesame set of commands. In other implementations, a program can refer to adiscrete batch or file.

Set forth below are some of the surprising effects of utilizing themethods and systems for performing image analysis set forth herein. Insome sequencing implementations, an important measure of a sequencingsystem's utility is its overall efficiency. For example, the amount ofmappable data produced per day and the total cost of installing andrunning the instrument are important aspects of an economical sequencingsolution. To reduce the time to generate mappable data and to increasethe efficiency of the system, real-time base calling can be enabled onan instrument computer and can run in parallel with sequencing chemistryand imaging. This allows much of the data processing and analysis to becompleted before the sequencing chemistry finishes. Additionally, it canreduce the storage required for intermediate data and limit the amountof data that needs to travel across the network.

While sequence output has increased, the data per run transferred fromthe systems provided herein to the network and to secondary analysisprocessing hardware has substantially decreased. By transforming data onthe instrument computer (acquiring computer), network loads aredramatically reduced. Without these on-instrument, off-network datareduction techniques, the image output of a fleet of DNA sequencinginstruments would cripple most networks.

The widespread adoption of the high-throughput DNA sequencinginstruments has been driven in part by ease of use, support for a rangeof applications, and suitability for virtually any lab environment. Thehighly efficient algorithms presented herein allow significant analysisfunctionality to be added to a simple workstation that can controlsequencing instruments. This reduction in the requirements forcomputational hardware has several practical benefits that will becomeeven more important as sequencing output levels continue to increase.For example, by performing image analysis and base calling on a simpletower, heat production, laboratory footprint, and power consumption arekept to a minimum. In contrast, other commercial sequencing technologieshave recently ramped up their computing infrastructure for primaryanalysis, with up to five times more processing power, leading tocommensurate increases in heat output and power consumption. Thus, insome implementations, the computational efficiency of the methods andsystems provided herein enables customers to increase their sequencingthroughput while keeping server hardware expenses to a minimum.

Accordingly, in some implementations, the methods and/or systemspresented herein act as a state machine, keeping track of the individualstate of each specimen, and when it detects that a specimen is ready toadvance to the next state, it does the appropriate processing andadvances the specimen to that state. A more detailed example of how thestate machine monitors a file system to determine when a specimen isready to advance to the next state according to a preferredimplementation is set forth in Example 1 below.

In preferred implementations, the methods and systems provided hereinare multi-threaded and can work with a configurable number of threads.Thus, for example in the context of nucleic acid sequencing, the methodsand systems provided herein are capable of working in the backgroundduring a live sequencing run for real-time analysis, or it can be runusing a pre-existing set of image data for off-line analysis. In certainpreferred implementations, the methods and systems handlemulti-threading by giving each thread its own subset of specimen forwhich it is responsible. This minimizes the possibility of threadcontention.

A method of the present disclosure can include a step of obtaining atarget image of an object using a detection apparatus, wherein the imageincludes a repeating pattern of analytes on the object. Detectionapparatus that are capable of high resolution imaging of surfaces areparticularly useful. In particular implementations, the detectionapparatus will have sufficient resolution to distinguish analytes at thedensities, pitches, and/or analyte sizes set forth herein. Particularlyuseful are detection apparatus capable of obtaining images or image datafrom surfaces. Example detectors are those that are configured tomaintain an object and detector in a static relationship while obtainingan area image. Scanning apparatus can also be used. For example, anapparatus that obtains sequential area images (e.g., so called ‘step andshoot’ detectors) can be used. Also useful are devices that continuallyscan a point or line over the surface of an object to accumulate data toconstruct an image of the surface. Point scanning detectors can beconfigured to scan a point (i.e., a small detection area) over thesurface of an object via a raster motion in the x-y plane of thesurface. Line scanning detectors can be configured to scan a line alongthe y dimension of the surface of an object, the longest dimension ofthe line occurring along the x dimension. It will be understood that thedetection device, object or both can be moved to achieve scanningdetection. Detection apparatus that are particularly useful, for examplein nucleic acid sequencing applications, are described in US Pat App.Pub. Nos. 2012/0270305 A1; 2013/0023422 A1; and 2013/0260372 A1; andU.S. Pat. Nos. 5,528,050; 5,719,391; 8,158,926 and 8,241,573, each ofwhich is incorporated herein by reference.

The implementations disclosed herein may be implemented as a method,apparatus, system, or article of manufacture using programming orengineering techniques to produce software, firmware, hardware, or anycombination thereof. The term “article of manufacture” as used hereinrefers to code or logic implemented in hardware or computer readablemedia such as optical storage devices, and volatile or non-volatilememory devices. Such hardware may include, but is not limited to, fieldprogrammable gate arrays (FPGAs), coarse grained reconfigurablearchitectures (CGRAs), application-specific integrated circuits (ASICs),complex programmable logic devices (CPLDs), programmable logic arrays(PLAs), microprocessors, or other similar processing devices. Inparticular implementations, information or algorithms set forth hereinare present in non-transient storage media.

In particular implementations, a computer implemented method set forthherein can occur in real time while multiple images of an object arebeing obtained. Such real time analysis is particularly useful fornucleic acid sequencing applications wherein an array of nucleic acidsis subjected to repeated cycles of fluidic and detection steps. Analysisof the sequencing data can often be computationally intensive such thatit can be beneficial to perform the methods set forth herein in realtime or in the background while other data acquisition or analysisalgorithms are in process. Example real time analysis methods that canbe used with the present methods are those used for the MiSeq and HiSeqsequencing devices commercially available from Illumina, Inc. (SanDiego, Calif.) and/or described in US Pat. App. Pub. No. 2012/0020537A1, which is incorporated herein by reference.

An example data analysis system, formed by one or more programmedcomputers, with programming being stored on one or more machine readablemedia with code executed to carry out one or more steps of methodsdescribed herein. In one implementation, for example, the systemincludes an interface designed to permit networking of the system to oneor more detection systems (e.g., optical imaging systems) that areconfigured to acquire data from target objects. The interface mayreceive and condition data, where appropriate. In particularimplementations the detection system will output digital image data, forexample, image data that is representative of individual pictureelements or pixels that, together, form an image of an array or otherobject. A processor processes the received detection data in accordancewith a one or more routines defined by processing code. The processingcode may be stored in various types of memory circuitry.

In accordance with the presently contemplated implementations, theprocessing code executed on the detection data includes a data analysisroutine designed to analyze the detection data to determine thelocations and metadata of individual analytes visible or encoded in thedata, as well as locations at which no analyte is detected (i.e., wherethere is no analyte, or where no meaningful signal was detected from anexisting analyte). In particular implementations, analyte locations inan array will typically appear brighter than non-analyte locations dueto the presence of fluorescing dyes attached to the imaged analytes. Itwill be understood that the analytes need not appear brighter than theirsurrounding area, for example, when a target for the probe at theanalyte is not present in an array being detected. The color at whichindividual analytes appear may be a function of the dye employed as wellas of the wavelength of the light used by the imaging system for imagingpurposes. Analytes to which targets are not bound or that are otherwisedevoid of a particular label can be identified according to othercharacteristics, such as their expected location in the microarray.

Once the data analysis routine has located individual analytes in thedata, a value assignment may be carried out. In general, the valueassignment will assign a digital value to each analyte based uponcharacteristics of the data represented by detector components (e.g.,pixels) at the corresponding location. That is, for example when imagingdata is processed, the value assignment routine may be designed torecognize that a specific color or wavelength of light was detected at aspecific location, as indicated by a group or cluster of pixels at thelocation. In a typical DNA imaging application, for example, the fourcommon nucleotides will be represented by four separate anddistinguishable colors. Each color, then, may be assigned a valuecorresponding to that nucleotide.

As used herein, the terms “module”, “system,” or “system controller” mayinclude a hardware and/or software system and circuitry that operates toperform one or more functions. For example, a module, system, or systemcontroller may include a computer processor, controller, or otherlogic-based device that performs operations based on instructions storedon a tangible and non-transitory computer readable storage medium, suchas a computer memory. Alternatively, a module, system, or systemcontroller may include a hard-wired device that performs operationsbased on hard-wired logic and circuitry. The module, system, or systemcontroller shown in the attached figures may represent the hardware andcircuitry that operates based on software or hardwired instructions, thesoftware that directs hardware to perform the operations, or acombination thereof. The module, system, or system controller caninclude or represent hardware circuits or circuitry that include and/orare connected with one or more processors, such as one or computermicroprocessors.

As used herein, the terms “software” and “firmware” are interchangeableand include any computer program stored in memory for execution by acomputer, including RAM memory, ROM memory, EPROM memory, EEPROM memory,and non-volatile RAM (NVRAM) memory. The above memory types are examplesonly and are thus not limiting as to the types of memory usable forstorage of a computer program.

In the molecular biology field, one of the processes for nucleic acidsequencing in use is sequencing-by-synthesis. The technique can beapplied to massively parallel sequencing projects. For example, by usingan automated platform, it is possible to carry out hundreds of thousandsof sequencing reactions simultaneously. Thus, one of the implementationsof the present invention relates to instruments and methods foracquiring, storing, and analyzing image data generated during nucleicacid sequencing.

Enormous gains in the amount of data that can be acquired and storedmake streamlined image analysis methods even more beneficial. Forexample, the image analysis methods described herein permit bothdesigners and end users to make efficient use of existing computerhardware. Accordingly, presented herein are methods and systems whichreduce the computational burden of processing data in the face ofrapidly increasing data output. For example, in the field of DNAsequencing, yields have scaled 15-fold over the course of a recent yearand can now reach hundreds of gigabases in a single run of a DNAsequencing device. If computational infrastructure requirements grewproportionately, large genome-scale experiments would remain out ofreach to most researchers. Thus, the generation of more raw sequencedata will increase the need for secondary analysis and data storage,making optimization of data transport and storage extremely valuable.Some implementations of the methods and systems presented herein canreduce the time, hardware, networking, and laboratory infrastructurerequirements needed to produce usable sequence data.

The present disclosure describes various methods and systems forcarrying out the methods. Examples of some of the methods are describedas a series of steps. However, it should be understood thatimplementations are not limited to the particular steps and/or order ofsteps described herein. Steps may be omitted, steps may be modified,and/or other steps may be added. Moreover, steps described herein may becombined, steps may be performed simultaneously, steps may be performedconcurrently, steps may be split into multiple sub-steps, steps may beperformed in a different order, or steps (or a series of steps) may bere-performed in an iterative fashion. In addition, although differentmethods are set forth herein, it should be understood that the differentmethods (or steps of the different methods) may be combined in otherimplementations.

In some implementations, a processing unit, processor, module, orcomputing system that is “configured to” perform a task or operation maybe understood as being particularly structured to perform the task oroperation (e.g., having one or more programs or instructions storedthereon or used in conjunction therewith tailored or intended to performthe task or operation, and/or having an arrangement of processingcircuitry tailored or intended to perform the task or operation). Forthe purposes of clarity and the avoidance of doubt, a general purposecomputer (which may become “configured to” perform the task or operationif appropriately programmed) is not “configured to” perform a task oroperation unless or until specifically programmed or structurallymodified to perform the task or operation.

Moreover, the operations of the methods described herein can besufficiently complex such that the operations cannot be mentallyperformed by an average human being or a person of ordinary skill in theart within a commercially reasonable time period. For example, themethods may rely on relatively complex computations such that such aperson cannot complete the methods within a commercially reasonabletime.

Throughout this application various publications, patents or patentapplications have been referenced. The disclosures of these publicationsin their entireties are hereby incorporated by reference in thisapplication in order to more fully describe the state of the art towhich this invention pertains.

The term “comprising” is intended herein to be open-ended, including notonly the recited elements, but further encompassing any additionalelements.

As used herein, the term “each”, when used in reference to a collectionof items, is intended to identify an individual item in the collectionbut does not necessarily refer to every item in the collection.Exceptions can occur if explicit disclosure or context clearly dictatesotherwise.

Although the invention has been described with reference to the examplesprovided above, it should be understood that various modifications canbe made without departing from the invention.

The modules in this application can be implemented in hardware orsoftware and need not be divided up in precisely the same blocks asshown in the figures. Some can also be implemented on differentprocessors or computers or spread among a number of different processorsor computers. In addition, it will be appreciated that some of themodules can be combined, operated in parallel or in a different sequencethan that shown in the figures without affecting the functions achieved.Also as used herein, the term “module” can include “sub-modules”, whichthemselves can be considered herein to constitute modules. The blocks inthe figures designated as modules can also be thought of as flowchartsteps in a method.

As used herein, the “identification” of an item of information does notnecessarily require the direct specification of that item ofinformation. Information can be “identified” in a field by simplyreferring to the actual information through one or more layers ofindirection, or by identifying one or more items of differentinformation which are together sufficient to determine the actual itemof information. In addition, the term “specify” is used herein to meanthe same as “identify”.

As used herein, a given signal, event or value is “in dependence upon” apredecessor signal, event or value of the predecessor signal, event orvalue influenced by the given signal, event, or value. If there is anintervening processing element, step or time period, the given signal,event, or value can still be “in dependence upon” the predecessorsignal, event, or value. If the intervening processing element or stepcombines more than one signal, event or value, the signal output of theprocessing element or step is considered “in dependence upon” each ofthe signal, event, or value inputs. If the given signal, event, or valueis the same as the predecessor signal, event, or value, this is merely adegenerate case in which the given signal, event or value is stillconsidered to be “in dependence upon” or “dependent on” or “based on”the predecessor signal, event, or value. “Responsiveness” of a givensignal, event or value upon another signal, event or value is definedsimilarly.

As used herein, “concurrently” or “in parallel” does not require exactsimultaneity. It is sufficient if the evaluation of one of theindividuals begins before the evaluation of another of the individualscompletes.

This application refers to “sequencing images,” “cluster images” and“cluster intensity images” interchangeably.

Other Implementations

In one implementation, for the current sequencing cycle, the state datacan be redundantly provided as input to the base caller 144 usingresidual connections and/or skip connections, for example, tointermediate layers of the neural network-based base caller 2900. Insome implementations, the residual connections and/or skip connectionsprovide the state data as input in addition to the state data beingprovided as input to a first layer of the neural network-based basecaller 2900.

In one implementation, for the current sequencing cycle, the state datacan be determined from a subset of previous sequencing cycles, asopposed to all the previous sequencing cycles.

In one implementation, for the current sequencing cycle, multipleinstances of the state data can be determined such that each instance isdetermined from a different subset of the previous sequencing cycles.

In one implementation, for the current sequencing cycle, multiple copiesof the state data can be provided as input to the base caller 144, forexample, as multiple input channels.

In some implementations, the state information can be provided as inputto the temporal layers.

Performance Results as Objective Indicia of Inventiveness andNon-Obviousness

FIG. 45 compares base calling performance of a non-neural network-basedbase caller RTA, a neural network-based base caller DeepRTA (5ci_k14)without state information, and the disclosed state-based and neuralnetwork-based base caller DeepRTA with State (5ci_k14_DC) that usesper-pixel MIN state information as additional/supplemental input. InFIG. 45 , the x-axis denotes the progression of sequencing cycles of asequencing run, and the y-axis denotes the base calling error rate.Also, FIG. 45 has two charts for sequencing of a first read (Read 1) andsequencing of a second read (Read 2).

As demonstrated, DeepRTA with State's base call accuracy exhibits 10%improvement over DeepRTA without state information in Read 1, and 8%improvement in Read 2. DeepRTA with State also significantly outperformsRTA.

FIG. 46 compares base calling performance of the disclosed state-basedand neural network-based base caller DeepRTA with State (5ci_k14_DC)across different state channels. In FIG. 46 , the different statechannels are DC(MIN) blue, DC(MIN)_green, DC(MIN)_avg, DC(MIN)_min, andDC(MIN). The DC(MIN) blue state channel is a single channel thatincludes per-pixel MIN state values only for the blue channel. TheDC(MIN)_green state channel is a single channel that includes per-pixelMIN state values only for the green channel. The DC(MIN)_avg statechannel is a single channel that includes per-pixel and pan-channel AVGstate values determined as an average of the MIN state values of theblue and green channels. The DC(MIN)_min state channel is a singlechannel that includes per-pixel and pan-channel MIN state valuesselected from the MIN state values of the blue and green channels. TheDC(MIN) state channels are two channels-one that includes per-pixel andper-channel MIN state values for the blue channel and another thatincludes per-pixel and per-channel MIN state values for the greenchannel.

With the DC(MIN) state channels as supplemental input, DeepRTA withState's base call accuracy exhibits 10% improvement over DeepRTA inRead 1. With the DC(MIN)_blue state channel as supplemental input,DeepRTA with State's base call accuracy exhibits 7% improvement overDeepRTA in Read 1. With the DC(MIN)_green state channel as supplementalinput, DeepRTA with State's base call accuracy exhibits 4% improvementover DeepRTA in Read 1. With the DC(MIN)_avg state channel assupplemental input, DeepRTA with State's base call accuracy exhibits 8%improvement over DeepRTA in Read 1. With the DC(MIN)_min state channelas supplemental input, DeepRTA with State's base call accuracy exhibits7% improvement over DeepRTA in Read 1.

FIG. 47 illustrates base calling performance of the disclosedstate-based and neural network-based base caller DeepRTA with State (DC)on k-mers, specifically 5mers. 5mers refers to repetitive base patternsfor five base positions (e.g., ACGCG, GGGGG, TCGCG). In FIG. 47 , thex-axis denotes all 1024 combinations of the 5mers, and the y-axisdenotes base calling performance of the disclosed state-based and neuralnetwork-based base caller DeepRTA with State (DC).

In particular, the y-axis compares the base calling errors of thedisclosed state-based and neural network-based base caller DeepRTA withState (DC) against the base calling errors of the DeepRTA without stateinformation, such that the smaller the base calling errors of thedisclosed state-based and neural network-based base caller DeepRTA withState (DC), the larger the negative values along the y-axis. As shown,the highly error-prone k-mer of Gs (due to lack of active states (i.e.,only dark states of Gs)) experiences a small base calling error rate4702 with the disclosed state-based and neural network-based base callerDeepRTA with State (DC).

FIG. 48 compares base calling performance of a non-neural network-basedbase caller RTA, a neural network-based base caller DeepRTA withoutstate information (5ci_k14), the disclosed state-based and neuralnetwork-based base caller DeepRTA with State (5ci_k14_DC_MIN) that usesper-pixel MIN state information as additional/supplemental input, andthe disclosed state-based and neural network-based base caller DeepRTAwith State (5ci_k14_DC_MIN_MAX) that uses per-pixel MIN and MAX stateinformation as additional/supplemental input.

FIG. 48 shows that the DeepRTA without state information (5ci_k14)demonstrates 32% improvement over the RTA, the disclosed state-based andneural network-based base caller DeepRTA with State (5ci_k14_DC_MIN)that uses per-pixel MIN state information as additional/supplementalinput demonstrates 39% improvement over the RTA, and the disclosedstate-based and neural network-based base caller DeepRTA with State(5ci_k14_DC_MIN_MAX) that uses per-pixel MIN and MAX state informationas additional/supplemental input demonstrates 53% improvement over theRTA.

FIG. 49 compares base calling performance of a non-neural network-basedbase caller RTA, the neural network-based base caller DeepRTA withoutstate information and 5-cycle flanking input window (5ci_k14), thedisclosed state-based and neural network-based base caller DeepRTA withState (5ci_k14_DC_MIN) that uses per-pixel MIN state information asadditional/supplemental input along with 5-cycle flanking input window,the disclosed state-based and neural network-based base caller DeepRTAwith State (3ci_k14_DC_MIN_MAX) that uses per-pixel MIN and MAX stateinformation as additional/supplemental input along with 3-cycle flankinginput window, and the disclosed state-based and neural network-basedbase caller DeepRTA with State (5ci_k14_DC_MIN_MAX) that uses per-pixelMIN and MAX state information as additional/supplemental input alongwith 5-cycle flanking input window.

FIG. 49 shows that the disclosed state-based and neural network-basedbase caller DeepRTA with State (3ci_k14_DC_MIN_MAX) that uses per-pixelMIN and MAX state information as additional/supplemental input alongwith 3-cycle flanking input window demonstrates 45% improvement over theRTA. This shows that even with a smaller input window compared to theDeepRTA without state information (5ci_k14) (and the DeepRTA with State(5ci_k14_DC_MIN) (both with 5-cycle flanking input window), the DeepRTAwith State (3ci_k14_DC_MIN_MAX) (3-cycle flanking input window) performsbetter. That is, with the state information, we get better base callingaccuracy with processing lesser image data and thereby saving compute.

FIG. 50 compares base calling performance of the disclosed state-basedand neural network-based base caller DeepRTA with State(5ci_k14_DC_MIN_MAX) that uses per-pixel MIN and MAX state informationas additional/supplemental input along with 5-cycle flanking inputwindow on k-mers (specifically 5mers) against that of the neuralnetwork-based base caller DeepRTA without state information and 5-cycleflanking input window (5ci_k14). 5mers refers to repetitive basepatterns for five base positions (e.g., ACGCG, GGGGG, TCGCG). In FIG. 50, the x-axis denotes all 1024 combinations of the 5mers, and the y-axisdenotes base calling performance of the disclosed state-based and neuralnetwork-based base caller DeepRTA with State (DC).

In particular, in FIG. 50 , the disclosed state-based and neuralnetwork-based base caller DeepRTA with State (5ci_k14_DC_MIN_MAX) thatuses per-pixel MIN and MAX state information as additional/supplementalinput along with 5-cycle flanking input window has longer negative linesthan the neural network-based base caller DeepRTA without stateinformation and 5-cycle flanking input window (5ci_k14), which indicatesthe disclosed state-based and neural network-based base caller DeepRTAwith State (5ci_k14_DC_MIN_MAX) that uses per-pixel MIN and MAX stateinformation as additional/supplemental input along with 5-cycle flankinginput window has a lower base calling error rate for the 5mers along thex-axis.

FIG. 51 compares base calling performance of a non-neural network-basedbase caller RTA with an Equalizer implementation, the disclosedstate-based and neural network-based base caller DeepRTA with State(5ci_k14_DC_MIN_MAX) that uses per-pixel MIN and MAX state informationas additional/supplemental input along with 5-cycle flanking inputwindow, and the disclosed state-based and neural network-based basecaller DeepRTA with State (5ci_k14_DC_AVG) that uses per-pixel AVG stateinformation as additional/supplemental input along with 5-cycle flankinginput window.

FIG. 51 shows that the disclosed state-based and neural network-basedbase caller DeepRTA with State (5ci_k14_DC_MIN_MAX) that uses per-pixelMIN and MAX state information as additional/supplemental input alongwith 5-cycle flanking input window demonstrates 36.7% improvement overthe RTA with the Equalizer implementation. FIG. 51 also shows that thedisclosed state-based and neural network-based base caller DeepRTA withState (5ci_k14_DC_AVG) that uses per-pixel AVG state information asadditional/supplemental input along with 5-cycle flanking input windowdemonstrates 34.6% improvement over the RTA with the Equalizerimplementation.

FIG. 52 compares base calling performance of the non-neuralnetwork-based base caller RTA with the Equalizer implementation and thedisclosed state-based and neural network-based base caller DeepRTA withState (5ci_k14_DC_MIN_MAX_AVG) that uses per-pixel MIN, MAX, and AVGstate information as additional/supplemental input along with 5-cycleflanking input window.

FIG. 52 shows that the disclosed state-based and neural network-basedbase caller DeepRTA with State (5ci_k14_DC_MIN_MAX_AVG) that usesper-pixel MIN, MAX, and AVG state information as additional/supplementalinput along with 5-cycle flanking input window demonstrates 36.9%improvement over the RTA with the Equalizer implementation.

FIG. 53 compares base calling performance of the non-neuralnetwork-based base caller RTA with the Equalizer implementation, thedisclosed state-based and neural network-based base caller DeepRTA withState (5ci_k14_DC_MIN_MAX) that uses per-pixel MIN and MAX stateinformation as additional/supplemental input along with 5-cycle flankinginput window and filter banks of size 14 (K=14), and the disclosedstate-based and neural network-based base caller DeepRTA with State(5ci_k14_DC_MIN_MAX) that uses per-pixel MIN and MAX state informationas additional/supplemental input along with 5-cycle flanking inputwindow and filter banks of size 32 (K=32).

FIG. 53 shows that the disclosed state-based and neural network-basedbase caller DeepRTA with State (5ci_k14_DC_MIN_MAX) that uses per-pixelMIN and MAX state information as additional/supplemental input alongwith 5-cycle flanking input window and filter banks of size 14 (K=14)demonstrates 36.7% improvement over the RTA with the Equalizerimplementation for Read 1 and 27.2% improvement for Read 2. FIG. 53 alsoshows that the disclosed state-based and neural network-based basecaller DeepRTA with State (5ci_k14_DC_MIN_MAX) that uses per-pixel MINand MAX state information as additional/supplemental input along with5-cycle flanking input window and filter banks of size 32 (K=32)demonstrates 44.8% improvement over the RTA with the Equalizerimplementation for Read 1 and 36.9% improvement for Read 2.

FIG. 54 compares base calling performance of the disclosed state-basedand neural network-based base caller DeepRTA with State for differentalpha parameter configurations (e.g., 0.05, 0.07, 0.10, 0.12) ofexponential weighted averaging implementations, as discussed above. Asshown, the alpha parameter of 0.12 has the best base calling accuracyfor both Read 1 and Read 2.

FIGS. 55 and 56 are graphs that track the state values determined byexponential weighted averages according to one implementation of thetechnology disclosed. In FIGS. 55 and 56 , the blue lines are the rawintensity, and the exponentially weighted average estimate isinitialized at cycle 21 using the average intensity from cycles 1-20 andan alpha=0.02. The x-axis is the cycle index, and the y-axis is measuredamplitude (blue) plus estimated average intensity (orange). Theestimated amplitude in orange follows the envelope of theintensity/average intensity as desired. It is moderately noisy, due tothe need to average over ON/OFF intensities to recover averageintensity. In the next example, the amplitude suddenly steps down atcycle 75. The exponential moving average is able to estimate thisamplitude change quite quickly. The pixel intensity average is quite anoisy statistic since it has to average over the large ON/OFF steps aswell as smaller amplitude variations.

FIG. 57 compares base calling performance of RTA with the Equalizerimplementation (RTA+Eq), the DeepRTA without state information (DeepRTA(k14_1m)), and the disclosed DeepRTA with State (DeepRTA_extrachannels(k14_1,_onoff_expavg)) from the perspective of secondary analysis tasksand metrics like number of called reads, read mismatch error rate forRead 1 and Read 1 and their average, single nucleotide polymorphism(SNP) recall, SNO precision, SNP calling accuracy (F1 score),insertion/deletion (Indel) recall, Indel precision, and Indel callingaccuracy (F1 score). The disclosed DeepRTA_extrachannels(k14_1,_onoff_expavg) model uses state information determined fromexponential weighted average implementations discussed above. Asdemonstrated, the disclosed DeepRTA_extrachannels (k14_1,_onoff_expavg)model outperforms others on a variety of secondary analysis tasks andmetrics.

CLAUSES

The technology disclosed can be practiced as a system, method, orarticle of manufacture. One or more features of an implementation can becombined with the base implementation. Implementations that are notmutually exclusive are taught to be combinable. One or more features ofan implementation can be combined with other implementations. Thisdisclosure periodically reminds the user of these options. Omission fromsome implementations of recitations that repeat these options should notbe taken as limiting the combinations taught in the precedingsections—these recitations are hereby incorporated forward by referenceinto each of the following implementations.

One or more implementations and clauses of the technology disclosed orelements thereof can be implemented in the form of a computer product,including a non-transitory computer readable storage medium withcomputer usable program code for performing the method steps indicated.Furthermore, one or more implementations and clauses of the technologydisclosed or elements thereof can be implemented in the form of anapparatus including a memory and at least one processor that is coupledto the memory and operative to perform exemplary method steps. Yetfurther, in another aspect, one or more implementations and clauses ofthe technology disclosed or elements thereof can be implemented in theform of means for carrying out one or more of the method steps describedherein; the means can include (i) hardware module(s), (ii) softwaremodule(s) executing on one or more hardware processors, or (iii) acombination of hardware and software modules; any of (i)-(iii) implementthe specific techniques set forth herein, and the software modules arestored in a computer readable storage medium (or multiple such media).

The clauses described in this section can be combined as features. Inthe interest of conciseness, the combinations of features are notindividually enumerated and are not repeated with each base set offeatures. The reader will understand how features identified in theclauses described in this section can readily be combined with sets ofbase features identified as implementations in other sections of thisapplication. These clauses are not meant to be mutually exclusive,exhaustive, or restrictive; and the technology disclosed is not limitedto these clauses but rather encompasses all possible combinations,modifications, and variations within the scope of the claimed technologyand its equivalents.

Other implementations of the clauses described in this section caninclude a non-transitory computer readable storage medium storinginstructions executable by a processor to perform any of the clausesdescribed in this section. Yet another implementation of the clausesdescribed in this section can include a system including memory and oneor more processors operable to execute instructions, stored in thememory, to perform any of the clauses described in this section.

We disclose the following clauses:

Clauses Set 1—Per-Pixel State Channels

1. A system, comprising:

-   -   memory storing, for a current sequencing cycle of a sequencing        run, respective current intensity values for respective pixels        in a plurality of pixels;    -   the memory storing, for one or more previous sequencing cycles        of the sequencing run that precede the current sequencing cycle,        sequences of respective previous intensity values for the        respective pixels;    -   a state generator, having access to the memory, configured to        generate respective current state values for the respective        pixels in dependence upon        -   (i) the respective current intensity values, and        -   (ii) respective previous intensity values in the sequences            of respective previous intensity values, and to store the            respective current state values in the memory; and    -   a base caller, having access to the memory, configured to        generate base calls for the current sequencing cycle in response        to processing        -   (i) the respective current intensity values, and        -   (ii) the respective current state values.            2. The system of clause 1, wherein respective intensity            values for the respective pixels are each characterized by            channel-specific intensity values for a plurality of            channels.            3. The system of clause 2, wherein respective state values            for the respective pixels are each characterized by            channel-specific state values for the plurality of channels.            4. The system of clause 3, wherein the respective current            state values are pixel-wise encoded with the respective            current intensity values.            5. The system of clause 3, wherein channel-specific state            values for a subset of channels in the plurality of channels            are pixel-wise encoded with the respective current intensity            values.            6. The system of clause 3, wherein the channel-specific            state values are averaged across channels in the plurality            of channels to generate respective pan-channel current state            values, wherein the respective pan-channel current state            values are pixel-wise encoded with the respective current            intensity values.            7. The system of clause 4, wherein the pixel-wise encoding            includes pixel-wise concatenation.            8. The system of clause 4, wherein the pixel-wise encoding            includes pixel-wise summing.            9. The system of clause 1, wherein the respective current            state values are respective current average intensities            determined for the respective pixels at the current            sequencing cycle from    -   (i) the respective previous intensity values, and    -   (ii) the respective current intensity values.        10. The system of clause 9, wherein the respective current        average intensities are each characterized by channel-specific        current average intensities.        11. The system of clause 10, wherein the respective current        state values are respective current maximum intensities        determined for the respective pixels at the current sequencing        cycle from    -   (i) the respective previous intensity values, and    -   (ii) the respective current intensity values.        12. The system of clause 11, wherein the respective current        maximum intensities are each characterized by channel-specific        current maximum intensities.        13. The system of clause 12, wherein the respective current        state values are respective current minimum intensities        determined for the respective pixels at the current sequencing        cycle from    -   (i) the respective previous intensity values, and    -   (ii) the respective current intensity values.        14. The system of clause 13, wherein the respective current        minimum intensities are each characterized by channel-specific        current minimum intensities.        15. The system of clause 14, wherein the respective current        state values are respective current exponentially weighted        average intensities determined for the respective pixels at the        current sequencing cycle from    -   (i) the respective previous intensity values, and    -   (ii) the respective current intensity values.        16. The system of clause 15, wherein the respective current        exponentially weighted average intensities are determined based        on weighting recent sequencing cycles more than earlier        sequencing cycles.        17. The system of clause 16, wherein the respective current        exponentially weighted average intensities are each        characterized by channel-specific current exponentially weighted        average intensities.        18. The system of clause 17, wherein the respective current        state values are respective current moving average intensities        determined for the respective pixels at the current sequencing        cycle from    -   (i) the respective current intensity values, and    -   (ii) a rolling subset of the respective previous intensity        values.        19. The system of clause 18, wherein the respective current        moving average intensities are each characterized by        channel-specific current moving average intensities.        20. The system of clause 19, wherein each of the respective        current intensity values is attributed to an active state bucket        or an inactive state bucket based on comparison of the        respective current intensity values to respective current active        state intensities and respective current inactive state        intensities.        21. The system of clause 20, wherein the respective current        active state intensities are respective current global maximum        intensities determined for the respective pixels at the current        sequencing cycle from    -   (i) the respective previous intensity values.        22. The system of clause 21, wherein the respective current        global maximum intensities are each characterized by        channel-specific current global maximum intensities.        23. The system of clause 22, wherein the respective current        inactive state intensities are respective current global minimum        intensities determined for the respective pixels at the current        sequencing cycle from    -   (i) the respective previous intensity values.        24. The system of clause 23, wherein the respective current        global minimum intensities are each characterized by        channel-specific current global minimum intensities.        25. The system of clause 24, wherein the respective current        state values further include respective current active state        values and respective current inactive state values generated        for the respective pixels at the current sequencing cycle.        26. The system of clause 25, wherein the respective current        active state values are each characterized by channel-specific        current active state values.        27. The system of clause 26, wherein the respective current        inactive state values are each characterized by channel-specific        current inactive state values.        28. The system of clause 27, wherein a current active state        value for a pixel in a subject channel is determined at the        current sequencing cycle from    -   (i) a current intensity value for the pixel in the subject        channel that is detected and attributed to the active state        bucket at the current sequencing cycle, and    -   (ii) previous intensity values for the pixel in the subject        channel that were detected at the previous sequencing cycles.        29. The system of clause 28, wherein the current intensity value        is attributed to the active state bucket based on comparison of        the current intensity value to a global maximum value and a        global minimum value determined from the previous intensity        values in the subject channel.        30. The system of clause 29, wherein the current intensity value        is attributed to the active state bucket based on comparison of        the current intensity value to a previous active state value and        a previous inactive state value determined at a preceding        sequencing cycle in the subject channel.        31. The system of clause 30, wherein the current active state        value is an exponentially weighted average determined from the        current intensity value and the previous intensity values.        32. The system of clause 31, wherein the current active state        value is an average determined from the current intensity value        and the previous intensity values.        33. The system of clause 32, wherein the current active state        value is a moving average determined from the current intensity        value and a rolling subset of the previous intensity values.        34. The system of clause 33, wherein the current active state        value is a minimum value determined from the current intensity        value and the previous intensity values.        35. The system of clause 34, wherein the current active state        value is a maximum value determined from the current intensity        value and the previous intensity values.        36. The system of clause 35, wherein the current active state        value is carried from the preceding sequencing cycle and not        redetermined at the current sequencing cycle when the current        intensity value is attributed to the inactive state bucket at        the current sequencing cycle.        37. The system of clause 36, wherein a current inactive state        value for the pixel in the subject channel is determined at the        current sequencing cycle from    -   (i) a current intensity value for the pixel in the subject        channel that is detected and attributed to the inactive state        bucket at the current sequencing cycle, and    -   (ii) previous intensity values for the pixel in the subject        channel that were detected at the previous sequencing cycles.        38. The system of clause 37, wherein the current intensity value        is attributed to the inactive state bucket based on comparison        of the current intensity value to a global maximum value and a        global minimum value determined from the previous intensity        values in the subject channel.        39. The system of clause 38, wherein the current intensity value        is attributed to the inactive state bucket based on comparison        of the current intensity value to a previous inactive state        value and a previous inactive state value determined at a        preceding sequencing cycle in the subject channel.        40. The system of clause 39, wherein the current inactive state        value is an exponentially weighted average determined from the        current intensity value and the previous intensity values.        41. The system of clause 40, wherein the current inactive state        value is an average determined from the current intensity value        and the previous intensity values.        42. The system of clause 41, wherein the current inactive state        value is a moving average determined from the current intensity        value and a rolling subset of the previous intensity values.        43. The system of clause 42, wherein the current inactive state        value is a minimum value determined from the current intensity        value and the previous intensity values.        44. The system of clause 43, wherein the current inactive state        value is a maximum value determined from the current intensity        value and the previous intensity values.        45. The system of clause 44, wherein the current inactive state        value is carried from the preceding sequencing cycle and not        redetermined at the current sequencing cycle when the current        intensity value is attributed to the active state bucket at the        current sequencing cycle.        46. The system of clause 1, wherein the respective current state        values are iteratively generated at each sequencing cycle of the        sequencing run.        47. The system of clause 1, wherein the memory is further        configured to store, for next sequencing cycles of the        sequencing run, sequences of respective next intensity values        for the respective pixels.        48. The system of clause 47, wherein the base caller is further        configured to generate the base calls for the current sequencing        cycle in response to processing    -   (i) the respective current intensity values,    -   (ii) respective previous intensity values in the sequences of        respective previous intensity values for one or more of the        previous sequencing cycles,    -   (iii) respective next intensity values in the sequences of        respective next intensity values for one or more of the next        sequencing cycles, and    -   (iv) the respective state values.        49. The system of clause 48, wherein the respective state values        are pixel-wise encoded with respective previous intensity values        and the respective next intensity values.        50. The system of clause 3, wherein the channels correspond to a        combination of illumination with a specific laser and imaging        through a specific optical filter.        51. The system of clause 49, wherein the channels correspond to        filter wavelength bands.        52. The system of clause 51, wherein the channels correspond to        imaging events at a sequencing cycle.        53. The system of clause 1, wherein the base caller is a neural        network.        54. The system of clause 53, wherein the neural network is a        convolutional neural network.        55. The system of clause 54, wherein the convolutional neural        network includes a plurality of spatial convolution layers and a        plurality of temporal convolution layers.        56. The system of clause 3, wherein the respective state values        are configured to characterize historic intensity patterns of        the respective pixels.        57. The system of clause 56, wherein the historic intensity        patterns are configured to compensate for losses in base calling        accuracy of the base caller.        58. The system of clause 57, wherein the historic intensity        patterns are configured to compensate for losses in base calling        accuracy of the base caller when base calling bases for k-mers.        59. The system of clause 1, wherein the base calls for the        current sequencing cycle include base calls for one or more        clusters for which the respective current signal values and the        respective previous signal values are detected.        60. The system of clause 59, wherein the respective state values        are configured to discern respective signal profiles of the        clusters.        61. The system of clause 1, wherein the base caller is trained        using sequencing images generated offline for an        already-executed sequencing run.        62. The system of clause 61, wherein the respective state values        for the respective pixels are calculated offline for each        sequencing cycle of the already-executed sequencing run in        advance of base calling by the base caller.        63. The system of clause 62, wherein the base caller is trained        to use the respective state values to compensate for losses in        base calling accuracy.        64. A system, comprising:    -   memory storing, for a current sequencing cycle of a sequencing        run, respective current signal values for respective signal        units in a plurality of signal units;    -   the memory storing, for one or more previous sequencing cycles        of the sequencing run that precede the current sequencing cycle,        sequences of respective previous signal values for the        respective signal units;    -   a state generator, having access to the memory, configured to        generate respective current state values for the respective        signal units in dependence upon        -   (i) the respective current signal values, and        -   (ii) respective previous intensity values in the sequences            of respective previous intensity values, and to store the            respective current state values in the memory; and    -   a base caller, having access to the memory, configured to        generate base calls for the current sequencing cycle in response        to processing        -   (i) the respective current signal values, and        -   (ii) the respective current state values.            65. The system of clause 64, wherein the respective signal            units are respective pixels of an image.            66. The system of clause 65, wherein the respective current            signal values are respective current pixel intensities of            the respective pixels detected at the current sequencing            cycle.            67. The system of clause 66, wherein the respective previous            signal values are respective previous pixel intensities of            the respective pixels detected at the previous sequencing            cycles.            68. The system of clause 67, wherein the base calls for the            current sequencing cycle include base calls for one or more            clusters for which the respective current signal values and            the respective previous signal values are detected.            69. The system of clause 68, wherein the respective current            state values are configured to discern respective signal            profiles of the clusters.            70. The system of clause 69, wherein the memory is further            configured to store, for next sequencing cycles of the            sequencing run, sequences of respective next signal values            for the respective signal units.            71. The system of clause 70, wherein the base caller is            further configured to generate the base calls for the            current sequencing cycle in response to processing    -   (i) the respective current signal values,    -   (ii) respective previous signal values in the sequences of        respective previous signal values for one or more of the        previous sequencing cycles,    -   (iii) respective next signal values in the sequences of        respective next signal values for one or more of the next        sequencing cycles, and    -   (iv) the respective state values.        72. The system of clause 64, wherein the respective current        signal values are respective current voltage values detected at        the current sequencing cycle.        73. The system of clause 72, wherein the respective previous        signal values are respective previous voltage values detected at        the previous sequencing cycles.        74. The system of clause 64, wherein the respective current        signal values are respective current current values detected at        the current sequencing cycle.        75. The system of clause 74, wherein the respective previous        signal values are respective previous current values detected at        the previous sequencing cycles.        76. A system, comprising:    -   a neural network-based base caller configured to process a first        sliding window of sequencing cycles including sequencing images        for a center sequencing cycle, one or more right flanking        sequencing cycles, and one or more left flanking sequencing        cycles;    -   a state generator configured to determine per-pixel states of        the sequencing images in the first sliding window of sequencing        cycles based on (i) pixel intensities in a sequencing image for        the center sequencing cycle in the first sliding window of        sequencing cycles, and (ii) pixel intensities in sequencing        images for sequencing cycles preceding the center sequencing        cycle in the first sliding window of sequencing cycles; and    -   the neural network-based base caller further configured to        process the sequencing images in the first sliding window of        sequencing cycles along with the per-pixel states to base call        the center sequencing cycle in the first sliding window of        sequencing cycles.        77. The system of clause 76, wherein a second sliding window of        sequencing cycles includes sequencing images for a center        sequencing cycle, one or more right flanking sequencing cycles,        and one or more left flanking sequencing cycles,    -   wherein the state generator configured to determine per-pixel        states of the sequencing images in the second sliding window of        sequencing cycles based on (i) pixel intensities in a sequencing        image for the center sequencing cycle in the second sliding        window of sequencing cycles, and (ii) pixel intensities in        sequencing images for sequencing cycles preceding the center        sequencing cycle in the second sliding window of sequencing        cycles, and    -   wherein the neural network-based base caller is further        configured to process the sequencing images in the second        sliding window of sequencing cycles along with the per-pixel        states to base call the center sequencing cycle in the second        sliding window of sequencing cycles.        78. The system of clause 77, wherein a third sliding window of        sequencing cycles includes sequencing images for a center        sequencing cycle, one or more right flanking sequencing cycles,        and one or more left flanking sequencing cycles,    -   wherein the state generator configured to determine per-pixel        states of the sequencing images in the third sliding window of        sequencing cycles based on (i) pixel intensities in a sequencing        image for the center sequencing cycle in the third sliding        window of sequencing cycles, and (ii) pixel intensities in        sequencing images for sequencing cycles preceding the center        sequencing cycle in the third sliding window of sequencing        cycles, and    -   wherein the neural network-based base caller is further        configured to process the sequencing images in the third sliding        window of sequencing cycles along with the per-pixel states to        base call the center sequencing cycle in the third sliding        window of sequencing cycles.        79. A system, comprising:    -   a neural network-based base caller having a plurality of        intensity pattern detectors, intensity pattern detectors in the        plurality of intensity pattern detectors configured to execute a        subject base calling operation based on intensity pattern        detection from intensity values of pixels in a current sliding        window of sequencing images with losses in base calling accuracy        due to confinement of the intensity pattern detectors to the        current window of sequencing images;    -   intensity contextualization logic configured to determine        intensity context data based on (i) past intensity values of the        pixels in sequencing images preceding sequencing images in the        current sliding window of sequencing images, and (ii) target        intensity values of the pixels in a target sequencing image in        the current sliding window of sequencing images;    -   data flow logic configured to append the intensity context data        with the intensity values of the pixels to generate intensity        contextualized pixels; and    -   the neural network-based base caller further configured to apply        the intensity pattern detectors on the intensity contextualized        pixels and generate base calls for the subject base calling        operation, the intensity context data in the intensity        contextualized pixels compensating for the losses in the base        calling accuracy.        80. A computer-implemented method, including:    -   accessing current window sequenced signal data for a first        plurality of sequencing cycles in a current sliding window of        sequencing cycles;    -   accessing previous sequenced signal data for a second plurality        of sequencing cycles that precede sequencing cycles in the first        plurality of sequencing cycles;    -   generating state signal data based on the current window        sequenced signal data and the previous sequenced signal data;        and    -   base calling at least one sequencing cycle in the first        plurality of sequencing cycles in response to processing the        current window sequenced signal data and the state signal data.        81. A computer-implemented method, including:    -   base calling a current sequencing cycle of a sequencing run        based on state information determined from one or more previous        sequencing cycles of the sequencing run that precede the current        sequencing cycle.        82. A system, comprising:    -   memory storing current sequencing images for clusters for a        current sequencing cycle of a sequencing run, and previous        sequencing images for the clusters for one or more previous        sequencing cycles of the sequencing run that precede the current        sequencing cycle;    -   a host processor having access to the memory and configured to        generate, for the current sequencing cycle, current per-pixel        states for pixels in the sequencing images based on the current        sequencing images and the previous sequencing images;    -   a configurable processor having access to the memory and        configured to execute a neural network to produce base call        classification scores; and    -   a data flow logic having access to the memory, the host        processor, and the configurable processor and configured        -   to provide the current per-pixel states and the current            sequencing images to the neural network and cause the neural            network to produce current base call classification scores            for the clusters for the current sequencing cycle in            response to processing the current per-pixel states and the            current sequencing images, and        -   to provide the current base call classification scores to            the host.            83. The system of clause 82, wherein the memory further            stores previous per-pixel states for the pixels generated by            the host processor in a previous sequencing cycle.            84. The system of clause 83, wherein the host processor is            further configured to generate the current per-pixel states            based on the current sequencing images and the previous            per-pixel states.

Clauses Set 2—Per-Well State Channels

1. A system, comprising:

-   -   a spatial convolutional neural network configured to process        sequencing images of analytes, and produce spatially convolved        features;    -   filtering logic configured to select, from the spatially        convolved features, a subset of spatially convolved features        that contain centers of the analytes;    -   state generation logic configured to generate per-analyte states        for the subset of spatially convolved features; temporal        processing logic configured to process the subset of spatially        convolved features and the per-analyte states, and produce a        temporally compact output; and    -   base calling logic configured to produce base calls based on the        temporally compact output.        2. The system of clause 1, wherein the per-analyte states are        intensity variation correction coefficients generated on an        analyte-by-analyte basis by a linear model in response to        independently processing the sequencing images.        3. The system of clause 2, wherein the intensity variation        correction coefficients include an amplification coefficient        that compensates for scale variation between the intensity        profiles of the analytes.        4. The system of clause 3, wherein the intensity variation        correction coefficients include channel-specific offset        coefficients that compensate for shift variation between the        intensity profiles of the analytes along a plurality of        channels.        5. The system of clause 4, wherein the intensity variation        correction coefficients include a common offset coefficient that        compensates for the shift variation.        6. The system of clause 5, wherein the intensity variation        correction coefficients are generated on the analyte-by-analyte        basis based on combining analysis of current intensity        statistics determined at a current sequencing cycle of a        sequencing run with analysis of historic intensity statistics        determined at one or more previous sequencing cycles of the        sequencing run that precede the current sequencing cycle.        7. The system of clause 1, wherein the intensity profiles of the        analytes are characterized by channel-specific intensity values        for channels in the plurality of channels.        8. The system of clause 7, wherein the per-analyte states are        characterized by channel-specific state values for the channels.        9. The system of clause 8, wherein the per-analyte states are        encoded with the subset of spatially convolved features on a        central feature-by-central feature basis.        10. The system of clause 8, wherein channel-specific state        values for a subset of the channels are encoded with the subset        of spatially convolved features on the central        feature-by-central feature basis.        11. The system of clause 8, wherein the channel-specific state        values are averaged across the channels to generate pan-channel        state values, wherein the pan-channel state values are encoded        with the subset of spatially convolved features on the central        feature-by-central feature basis.        12. The system of clause 9, wherein the per-analyte states are        concatenated with the subset of spatially convolved features on        the central feature-by-central feature basis.        13. The system of clause 9, wherein the per-analyte states are        summed with the subset of spatially convolved features on the        central feature-by-central feature basis.        14. The system of clause 1, wherein the per-analyte states are        based on feature values of the subset of spatially convolved        features produced by the spatial processing logic at the current        sequencing cycle and at the previous sequencing cycles.        15. The system of clause 14, wherein the per-analyte states are        averages of the feature values.        16. The system of clause 14, wherein the per-analyte states are        maximum ones of the feature values.        17. The system of clause 14, wherein the per-analyte states are        minimum ones of the feature values.        18. The system of clause 14, wherein the per-analyte states are        exponentially weighted averages of the feature values.        19. The system of clause 18, wherein the exponentially weighted        averages are determined based on weighting recent sequencing        cycles more than earlier sequencing cycles.        20. The system of clause 14, wherein the per-analyte states are        moving averages of the feature values.        21. The system of clause 20, wherein the moving averages use        rolling subsets of feature values from the previous sequencing        cycles.        22. The system of clause 14, wherein the per-analyte states        include active state values and inactive state values.        23. The system of clause 22, wherein the active state values and        inactive state values are channel-specific.        24. The system of clause 23, wherein the active state values and        inactive state values are determined from a preceding base call.        25. The system of clause 23, wherein the active state values and        inactive state values are determined based on global maximums        and global minimums of the feature values.        26. The system of clause 23, wherein the active state values and        inactive state values are determined based on exponentially        weighted averages of the feature values.        27. The system of clause 1, wherein the input includes a sliding        window of sequencing images for a current sequencing cycle, one        or more previous sequencing cycles, and one or more next        sequencing cycles.        28. The system of clause 27, wherein feature sets are extracted        from pixels of sequencing images in the sliding window of        sequencing images for the current sequencing cycle, the previous        sequencing cycles, and the next flanking sequencing cycles.        29. The system of clause 28, wherein central feature sets are        culled from the feature sets for the current sequencing cycle,        the previous sequencing cycles, and the next sequencing cycles.        30. The system of clause 29, wherein per-analyte state sets are        generated for and encoded with the central feature sets for the        current sequencing cycle, the previous sequencing cycles, and        the next flanking sequencing cycles.        31. The system of clause 1, wherein the spatial processing logic        and the temporal processing logic are neural networks.        32. The system of clause 31, wherein the neural networks are        convolutional neural networks.        33. The system of clause 1, wherein the per-analyte states are        configured to characterize historic intensity patterns of the        pixels.        34. The system of clause 33, wherein the historic intensity        patterns are configured to compensate for losses in base calling        accuracy.        35. The system of clause 34, wherein the historic intensity        patterns are configured to compensate for losses in base calling        accuracy when base calling bases for k-mers.        36. The system of clause 1, wherein the per-analyte states are        configured to discern the intensity profiles of the analytes.        37. The system of clause 1, wherein the spatial processing logic        and the temporal processing logic are trained using sequencing        images generated offline for an already-executed sequencing run.        38. The system of clause 37, wherein the per-analyte states are        calculated offline for each sequencing cycle of the        already-executed sequencing run in advance of base calling.        39. The system of clause 38, wherein the spatial processing        logic and the temporal processing logic are trained to use the        per-analyte states to compensate for losses in base calling        accuracy.        40. A system, comprising:    -   spatial processing logic configured to process an input in which        intensity profiles of analytes are dispersed across pixels of        sequencing images, and produce a spatially compact output in        which the intensity profiles of the analytes are aggregated into        features extracted from the pixels;    -   analyte focusing logic configured to cull those per-analyte,        central features from the features that characterize peak        intensities detected at centers of the analytes;    -   state generation logic configured to generate per-analyte states        for the per-analyte, central features;    -   temporal processing logic configured to process the per-analyte,        central features and the per-analyte states, and produce a        temporally compact output; and    -   base calling logic configured to produce base calls based on the        temporally compact output.

Clauses Set 3—Per-Compressed Feature State Channels

1. A system, comprising:

-   -   a spatial convolutional neural network configured to process        sequencing images of clusters, and produce spatially convolved        features;    -   filtering logic configured to select, from the spatially        convolved features, a subset of spatially convolved features        that contain centers of the clusters;    -   compression logic configured to compress the subset of spatially        convolved features into a set of compressed features, wherein        the subset of spatially convolved features has A channels, and        the set of compressed features has N channels, and wherein M>N;    -   contextualization logic configured to access state information        for compressed features in the set of compressed features, and        to append the state information with the compressed features to        generate a set of stateful compressed features;    -   a temporal convolutional neural network configured to process        the set of stateful compressed features, and produce temporally        convolved stateful features; and    -   base calling logic configured to generate base calls for the        clusters based on the temporally convolved stateful features.        2. The system of clause 1, wherein the per-channel states are        intensity variation correction coefficients generated on an        analyte-by-analyte basis by a linear model in response to        independently processing the sequencing images.        3. The system of clause 2, wherein the intensity variation        correction coefficients include an amplification coefficient        that compensates for scale variation between the intensity        profiles of the analytes.        4. The system of clause 3, wherein the intensity variation        correction coefficients include channel-specific offset        coefficients that compensate for shift variation between the        intensity profiles of the analytes along a plurality of        channels.        5. The system of clause 4, wherein the intensity variation        correction coefficients include a common offset coefficient that        compensates for the shift variation.        6. The system of clause 5, wherein the intensity variation        correction coefficients are generated on the analyte-by-analyte        basis based on combining analysis of current intensity        statistics determined at a current sequencing cycle of a        sequencing run with analysis of historic intensity statistics        determined at one or more previous sequencing cycles of the        sequencing run that precede the current sequencing cycle.        7. The system of clause 1, wherein the intensity profiles of the        analytes are characterized by channel-specific intensity values        for channels in the plurality of channels.        8. The system of clause 7, wherein the per-channel states are        characterized by channel-specific state values for the channels.        9. The system of clause 8, wherein the per-channel states are        encoded with the per-analyte, channel-specific bits on a        bit-by-bit basis.        10. The system of clause 8, wherein channel-specific state        values for a subset of the channels are encoded with the        per-analyte, channel-specific bits on the bit-by-bit basis.        11. The system of clause 8, wherein the channel-specific state        values are averaged across the channels to generate pan-channel        state values, wherein the pan-channel state values are encoded        with the per-analyte, channel-specific bits on the bit-by-bit        basis.        12. The system of clause 9, wherein the per-channel states are        concatenated with the per-analyte, channel-specific bits on the        bit-by-bit basis.        13. The system of clause 9, wherein the per-channel states are        summed with the per-analyte, channel-specific bits on the        bit-by-bit basis.        14. The system of clause 1, wherein the per-channel states are        based on compressed values of the per-analyte, channel-specific        bits produced by the spatial processing logic at the current        sequencing cycle and at the previous sequencing cycles.        15. The system of clause 14, wherein the per-channel states are        averages of the compressed values.        16. The system of clause 14, wherein the per-channel states are        maximum ones of the compressed values.        17. The system of clause 14, wherein the per-channel states are        minimum ones of the compressed values.        18. The system of clause 14, wherein the per-channel states are        exponentially weighted averages of the compressed values.        19. The system of clause 18, wherein the exponentially weighted        averages are determined based on weighting recent sequencing        cycles more than earlier sequencing cycles.        20. The system of clause 14, wherein the per-channel states are        moving averages of the compressed values.        21. The system of clause 20, wherein the moving averages use        rolling subsets of compressed values from the previous        sequencing cycles.        22. The system of clause 14, wherein the per-channel states        include active state values and inactive state values.        23. The system of clause 22, wherein the active state values and        inactive state values are channel-specific.        24. The system of clause 23, wherein the active state values and        inactive state values are determined from a preceding base call.        25. The system of clause 23, wherein the active state values and        inactive state values are determined based on global maximums        and global minimums of the compressed values.        26. The system of clause 23, wherein the active state values and        inactive state values are determined based on exponentially        weighted averages of the compressed values.        27. The system of clause 1, wherein the input includes a sliding        window of sequencing images for a current sequencing cycle, one        or more previous sequencing cycles, and one or more next        sequencing cycles.        28. The system of clause 27, wherein feature sets are extracted        from channel-specific pixels of sequencing images in the sliding        window of sequencing images for the current sequencing cycle,        the previous sequencing cycles, and the next flanking sequencing        cycles, wherein per-analyte, central feature sets are culled        from the feature sets for the current sequencing cycle, the        previous sequencing cycles, and the next sequencing cycles.        29. The system of clause 28, wherein channel-specific bit sets        are compressed from the per-analyte, central feature sets for        the current sequencing cycle, the previous sequencing cycles,        and the next sequencing cycles.        30. The system of clause 29, wherein per-channel state sets are        generated for and encoded with the channel-specific bit sets for        the current sequencing cycle, the previous sequencing cycles,        and the next flanking sequencing cycles.        31. The system of clause 1, wherein the spatial processing logic        and the temporal processing logic are neural networks.        32. The system of clause 31, wherein the neural networks are        convolutional neural networks.        33. The system of clause 1, wherein the per-channel states are        configured to characterize historic intensity patterns of the        channel-specific pixels.        34. The system of clause 33, wherein the historic intensity        patterns are configured to compensate for losses in base calling        accuracy.        35. The system of clause 34, wherein the historic intensity        patterns are configured to compensate for losses in base calling        accuracy when base calling bases for k-mers.        36. The system of clause 1, wherein the per-analyte states are        configured to discern the intensity profiles of the analytes.        37. The system of clause 1, wherein the spatial processing logic        and the temporal processing logic are trained using sequencing        images generated offline for an already-executed sequencing run.        38. The system of clause 37, wherein the per-channel states are        calculated offline for each sequencing cycle of the        already-executed sequencing run in advance of base calling.        39. The system of clause 38, wherein the spatial processing        logic and the temporal processing logic are trained to use the        per-channel states to compensate for losses in base calling        accuracy.        40. A system, comprising:    -   spatial processing logic configured to process an input in which        intensity profiles of analytes are dispersed across        channel-specific pixels of sequencing images, and produce a        spatially compact output in which the intensity profiles of the        analytes are aggregated into features extracted from the        channel-specific pixels;    -   analyte focusing logic configured to cull those per-analyte,        central features from the features that characterize peak        intensities detected at centers of the analytes;    -   compression logic configured to distill the per-analyte, central        features into per-analyte, channel-specific bits, wherein the        per-analyte, central features have M feature channels, wherein        the per-analyte, channel-specific bits have N bit channels, and        wherein M>N;    -   state generation logic configured to generate per-channel states        for the per-analyte, channel-specific bits;    -   temporal processing logic configured to process the per-analyte,        channel-specific bits and the per-channel states, and produce a        temporally compact output; and    -   base calling logic configured to produce base calls based on the        temporally compact output.

Clauses Set 4—Per-Well State to Per-Pixel State Transformation

1. A system, comprising:

-   -   per-analyte state generation logic configured to generate        per-analyte states in response to processing sequencing images        for analytes, and to encode the per-analyte states in a sparse        representation;    -   per-pixel state generation logic configured to transform the        per-analyte states into per-pixel states of pixels in the        sequencing images in response to processing the sparse        representation, and to encode the per-pixel states in a dense        representation; and a base caller configured to generate base        calls in response to processing the pixels in the sequencing        images supplemented with the per-pixel states encoded in the        dense representation.        2. The system of clause 1, wherein the per-pixel state        generation logic uses transposed convolutions.        3. The system of clause 2, wherein the transposed convolutions        are trained to transform the per-analyte states encoded in the        sparse representation into the per-pixel states encoded in the        dense representation.        4. The system of clause 2, wherein the base caller includes        spatial convolution layers and temporal convolution layers.        5. The system of clause 4, wherein the per-pixel states encoded        in the dense representation are pixel-wise encoded with the        pixels in the sequencing images for processing by a first        spatial convolution layer of the base caller.        6. The system of clause 5, wherein the per-pixel states encoded        in the dense representation are supplied as input to subsequent        spatial convolution layers of the base caller.        7. The system of clause 1, wherein the per-analyte states are        intensity variation correction coefficients generated on an        analyte-by-analyte basis by a linear model in response to        processing the sequencing images.        8. The system of clause 7, wherein the intensity variation        correction coefficients include an amplification coefficient        that compensates for scale variation between intensity profiles        of the analytes.        9. The system of clause 8, wherein the intensity variation        correction coefficients include channel-specific offset        coefficients that compensate for shift variation between the        intensity profiles of the analytes along a plurality of        channels.        10. The system of clause 9, wherein the intensity variation        correction coefficients include a common offset coefficient that        compensates for the shift variation.        11. The system of clause 10, wherein the intensity variation        correction coefficients are generated on the analyte-by-analyte        basis based on combining analysis of current intensity        statistics determined at a current sequencing cycle of a        sequencing run with analysis of historic intensity statistics        determined at one or more previous sequencing cycles of the        sequencing run that precede the current sequencing cycle.        12. The system of clause 1, wherein the intensity profiles of        the analytes are characterized by channel-specific intensity        values for channels in the plurality of channels.        13. The system of clause 12, wherein the per-analyte states are        characterized by channel-specific state values for the channels.        14. The system of clause 1, wherein the per-pixel state        generation logic uses interpolation.

What we claim is:
 1. A system, comprising: memory storing, for a currentsequencing cycle of a sequencing run, respective current intensityvalues for respective pixels in a plurality of pixels; the memorystoring, for one or more previous sequencing cycles of the sequencingrun that precede the current sequencing cycle, sequences of respectiveprevious intensity values for the respective pixels; a state generator,having access to the memory, configured to generate respective currentstate values for the respective pixels in dependence upon (i) therespective current intensity values, and (ii) respective previousintensity values in the sequences of respective previous intensityvalues, and to store the respective current state values in the memory;and a base caller, having access to the memory, configured to generatebase calls for the current sequencing cycle in response to processing(i) the respective current intensity values, and (ii) the respectivecurrent state values.
 2. The system of claim 1, wherein respectiveintensity values for the respective pixels are each characterized bychannel-specific intensity values for a plurality of channels.
 3. Thesystem of claim 2, wherein respective state values for the respectivepixels are each characterized by channel-specific state values for theplurality of channels.
 4. The system of claim 3, wherein the respectivecurrent state values are pixel-wise encoded with the respective currentintensity values.
 5. The system of claim 3, wherein channel-specificstate values for a subset of channels in the plurality of channels arepixel-wise encoded with the respective current intensity values.
 6. Thesystem of claim 3, wherein the channel-specific state values areaveraged across channels in the plurality of channels to generaterespective pan-channel current state values, wherein the respectivepan-channel current state values are pixel-wise encoded with therespective current intensity values.
 7. The system of claim 4, whereinthe pixel-wise encoding includes pixel-wise concatenation.
 8. The systemof claim 4, wherein the pixel-wise encoding includes pixel-wise summing.9. The system of claim 1, wherein the respective current state valuesare respective current average intensities determined for the respectivepixels at the current sequencing cycle from (i) the respective previousintensity values, and (ii) the respective current intensity values. 10.The system of claim 9, wherein the respective current averageintensities are each characterized by channel-specific current averageintensities.
 11. The system of claim 10, wherein the respective currentstate values are respective current maximum intensities determined forthe respective pixels at the current sequencing cycle from (i) therespective previous intensity values, and (ii) the respective currentintensity values.
 12. The system of claim 11, wherein the respectivecurrent maximum intensities are each characterized by channel-specificcurrent maximum intensities.
 13. The system of claim 12, wherein therespective current state values are respective current minimumintensities determined for the respective pixels at the currentsequencing cycle from (i) the respective previous intensity values, and(ii) the respective current intensity values.
 14. The system of claim13, wherein the respective current minimum intensities are eachcharacterized by channel-specific current minimum intensities.
 15. Thesystem of claim 14, wherein the respective current state values arerespective current exponentially weighted average intensities determinedfor the respective pixels at the current sequencing cycle from (i) therespective previous intensity values, and (ii) the respective currentintensity values.
 16. The system of claim 15, wherein the respectivecurrent exponentially weighted average intensities are determined basedon weighting recent sequencing cycles more than earlier sequencingcycles.
 17. The system of claim 16, wherein the respective currentexponentially weighted average intensities are each characterized bychannel-specific current exponentially weighted average intensities. 18.The system of claim 17, wherein the respective current state values arerespective current moving average intensities determined for therespective pixels at the current sequencing cycle from (i) therespective current intensity values, and (ii) a rolling subset of therespective previous intensity values.
 19. The system of claim 18,wherein the respective current moving average intensities are eachcharacterized by channel-specific current moving average intensities.20. The system of claim 19, wherein each of the respective currentintensity values is attributed to an active state bucket or an inactivestate bucket based on comparison of the respective current intensityvalues to respective current active state intensities and respectivecurrent inactive state intensities.
 21. The system of claim 20, whereinthe respective current active state intensities are respective currentglobal maximum intensities determined for the respective pixels at thecurrent sequencing cycle from (i) the respective previous intensityvalues.
 22. The system of claim 21, wherein the respective currentglobal maximum intensities are each characterized by channel-specificcurrent global maximum intensities.
 23. The system of claim 22, whereinthe respective current inactive state intensities are respective currentglobal minimum intensities determined for the respective pixels at thecurrent sequencing cycle from (i) the respective previous intensityvalues.
 24. The system of claim 23, wherein the respective currentglobal minimum intensities are each characterized by channel-specificcurrent global minimum intensities.
 25. The system of claim 24, whereinthe respective current state values further include respective currentactive state values and respective current inactive state valuesgenerated for the respective pixels at the current sequencing cycle. 26.The system of claim 25, wherein the respective current active statevalues are each characterized by channel-specific current active statevalues.
 27. The system of claim 26, wherein the respective currentinactive state values are each characterized by channel-specific currentinactive state values.
 28. The system of claim 27, wherein a currentactive state value for a pixel in a subject channel is determined at thecurrent sequencing cycle from (i) a current intensity value for thepixel in the subject channel that is detected and attributed to theactive state bucket at the current sequencing cycle, and (ii) previousintensity values for the pixel in the subject channel that were detectedat the previous sequencing cycles.
 29. A computer-implemented method,including: accessing current window sequenced signal data for a firstplurality of sequencing cycles in a current sliding window of sequencingcycles; accessing previous sequenced signal data for a second pluralityof sequencing cycles that precede sequencing cycles in the firstplurality of sequencing cycles; generating state signal data based onthe current window sequenced signal data and the previous sequencedsignal data; and base calling at least one sequencing cycle in the firstplurality of sequencing cycles in response to processing the currentwindow sequenced signal data and the state signal data.
 30. A system,comprising: memory storing current sequencing images for clusters for acurrent sequencing cycle of a sequencing run, and previous sequencingimages for the clusters for one or more previous sequencing cycles ofthe sequencing run that precede the current sequencing cycle; a hostprocessor having access to the memory and configured to generate, forthe current sequencing cycle, current per-pixel states for pixels in thesequencing images based on the current sequencing images and theprevious sequencing images; a configurable processor having access tothe memory and configured to execute a neural network to produce basecall classification scores; and a data flow logic having access to thememory, the host processor, and the configurable processor andconfigured to provide the current per-pixel states and the currentsequencing images to the neural network and cause the neural network toproduce current base call classification scores for the clusters for thecurrent sequencing cycle in response to processing the current per-pixelstates and the current sequencing images, and to provide the currentbase call classification scores to the host.